Poland, Poznan, 3–5 September 2014 · Kubacki Jan, Jędrzejczak Alina Small Area Estimation Under...

Book of abstracts

Poland, Poznan, 3–5 September 2014

3-5 September, Poznan, Poland

Contents

The Steering Board of the Conference . . . . . . . . . . . . . . . . . . . . . . . . . . . 7The Programme Committee of the Conference . . . . . . . . . . . . . . . . . . . . . . 7The Organizing Committee of the Conference . . . . . . . . . . . . . . . . . . . . . . 7The Programme of Small Area Estimation 2014 at a glance . . . . . . . . . . . . . . . 8The Programme of Small Area Estimation 2014 in detail . . . . . . . . . . . . . . . . 9Abstracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Arima Serena, Datta Gauri S., Liseo Brunero Multivariate Fay-HerriotModel With Structural Measurement Error . . . . . . . . . . . . . . . . . 20

Articus Charlotte, Munnich Ralf An Application Of Model Diagnostics ForThe Fay-Herriot-Model In The Context Of Estimating Regional RentalPrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Articus Charlotte, Burgard Jan Pablo, Munnich Ralf A Finite MixtureFay-Herriot Model For The Estimation Of Regional Rental Prices . . . . 22

Baldermann Claudia, Schmid Timo, Salvati Nicola A Robust Unit-LevelModel For Small Area Estimation Allowing For Spatial Non-Stationarity 23

Bell William R., Seiss Mark A Modeling Approach To Estimating The MeanSquared Error Of Synthetic Small Area Estimators . . . . . . . . . . . . 24

Beręsewicz Maciej Big Data For Small Area Estimation Or Small Area Esti-mation For Big Data? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Boonstra Harm Jan Hierarchical Bayesian Small Area Estimation With R . . 25Boubeta Martınez Miguel, Lombardıa Marıa Jose, Morales Domingo

Empirical Best Prediction In Poisson Mixed Models . . . . . . . . . . . . 26Breidt Jay F., Hernandez-Stumpfhauser Daniel, Opsomer Jean D.

Variational Approximations For Selecting Hierarchical Models Of Cir-cular Data In A Small Area Estimation Application . . . . . . . . . . . . 27

Buelens Bart, van den Brakel Jan Covariate Selection For Small Area Es-timation In Repeated Surveys . . . . . . . . . . . . . . . . . . . . . . . . 28

Burgard Jan Pablo, Munnich Ralf Teaching Sae Using Simulation . . . . . 29Chambers Raymond Two Recent Developments In Robust And Semiparamet-

ric Small Area Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 29Ciginas Andrius On A Concept Of Similarity . . . . . . . . . . . . . . . . . . 30Datta Gauri Sankar, Mandal Abhyuday Small Area Estimation With Un-

certain Random Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Dehnel Grażyna, Kowalewski Jacek The Tax Register And The Social Secu-

rity Register In Estimation Methodology Of Short-Term Business Statis-tics In Poland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3

Small Area Estimation 2014

El-Horbaty Yahia A Simple Score Test For Random Effects With ApplicationTo Small Area Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Esteban Maria Dolores, Morales Domingo, Perez Agustın Area-LevelTime Models In R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Fabrizi Enrico, Ferrante Maria Rosaria, Trivisano Carlo Estimation ofvalue added for firms cross-classified by region, industry and size usingrepeated survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Fabrizi Enrico, Ferrante Maria Rosaria, Trivisano Carlo HierarchicalBeta Regression Models For The Estimation Of Poverty And InequalityParameters In Small Areas . . . . . . . . . . . . . . . . . . . . . . . . . 35

Fasulo Andrea, D’Alo’ Michele, Di Biagio Lorenzo, Falorsi Stefano,Solari Fabrizio Benchmark constraints for space and time unit levelEBLUP estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Franco Carolina, Bell William R. Alternative Approaches To BorrowingInformation Over Time In Small Area Estimation With Application ToData From The Census Bureau’s American Community Survey . . . . . 39

Fuller Wayne A., Erciulescu Andreea L. Small Area Prediction Under Al-ternative Model Specifications . . . . . . . . . . . . . . . . . . . . . . . . 40

Ghosh Malay, Kubokawa Tatsuya, Kawakubo Yuki Benchmarked Empir-ical Bayes Estimators For Multiplicative Area Level Models . . . . . . . . 41

Gołata Elżbieta, Klimanek Tomasz Challenges Facing Academics And TheNsi In Sae Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Gruchociak Hanna Computational Problems In The Selection Of VariablesFor Multilevel Models Using Stepwise Regression . . . . . . . . . . . . . . 43

Haslett Stephen The Role Of Contextual Variables In Small Area EstimationOf Poverty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Hidiroglou Michel, Estevao Victor A Comparison Of Small Area And DirectEstimators Via Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Hobza Tomas, Morales Domingo Msm Estimation And Ebp In Unit-LevelLogit Mixed Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Józefowski Tomasz Applying Indirect Estimation To Special Economic Zones 46Karlberg Forough Small Area Prediction For Skewed Data In The Presence

Of Zeroes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Kawakubo Yuki, Kubokawa Tatsuya Modified Conditional Aic In Linear

Mixed Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Keto Mauno, Pahkinen Erkki On Sample Allocation For Efficient Small Area

Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Klimanek Tomasz Spatial Approach In Indirect Estimation Of Some Labor

Market Characteristics On Rural Areas . . . . . . . . . . . . . . . . . . . 51Kordos Jan Small Area Estimation In Official Statistics And Statistical Thinking 52Krapavickaite Danute, Rudys Tomas Application Of Small Area Estimation

Methods For Lithuanian Labor Force Survey Data . . . . . . . . . . . . . 53Kubacki Jan, Jędrzejczak Alina Small Area Estimation Under Spatial Sar

Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Lahiri Partha, Suntornchost Jiraphan Variable Selection For Linear Mixed

Models With Applications In Small Area Estimation . . . . . . . . . . . . 55

4


Lahiri Partha An Overview Of Small Area Estimation With Repeated SurveyData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Lehtonen Risto Experiences And Challenges In Teaching Small Area Estimation 57Luna Angela, Zhang Li-Chun Multivariate Generalized Structure Preserving

Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58López Vizcaıno Esther, Lombardıa Cortina Marıa Jose, Morales Domingo

Mme: An R Package For Small Area Estimation With Multinomial MixedModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Marchetti Stefano, Giusti Caterina, Salvati Nicola, Petrucci Alessan-dra Geographic Information In Area Level Models For Small Area Esti-mation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Małasiewicz Anna Estimation Of Education Level In 2011 National CensusOf Population And Housing In Poland Using Small Area Estimation . . 61

Mokhtarian Payam On Outlier Robust Small Area Prediction Of The Empir-ical Distribution Function . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Molina Isabel, Rao J.N.K. An Overview Of Small Area Estimation MethodsFor Poverty Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Montoya Imanol, Aramendi Jorge, Garmendia Ines, Iztueta AnjelesMixed Models For Longitudinal Data With Applications To Small AreaStatistics In The Basque Statistical Office . . . . . . . . . . . . . . . . . 63

Morales Domingo, Pagliarella Maria Chiara, Salvatore Renato Parti-tioned Area-Level Time Models For Estimating Poverty Indicators . . . . 64

Munnich Ralf Small Area Estimation In The German Census 2011 . . . . . . 65Munnich Ralf Small Area Applications: Some Remarks From A Design-Based

View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Nesa Mossamet Kamrun, Clark Robert G., Birrell Carole L. Adults

Health Status And Behaviors In New Zealand: An Application Of Multi-variate Fay-Herriot Model . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Okrasa Włodzimierz Spatial Dynamics Of Community Well-Being. PatternsOf Inequality Of Local Deprivation, Poland 2004-2012. . . . . . . . . . . 67

Okupniak Magdalena Mincer Model In Small Area Estimation . . . . . . . . 68Paradysz Jan, Paradysz Karolina Indirect Estimation Of Disability On The

Base Of Polish National Census 2011 . . . . . . . . . . . . . . . . . . . . 69Pfeffermann Danny Model Selection And Checking For Small Area Estmation 70Potrykowska Anna New Pattern Of International Migration In Poland. A

Migration Policy Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 71Pratesi Monica, Giannotti Fosca, Giusti Caterina, Marchetti Stefano,

Pedreschi Dino, Salvati Nicola Area Level Sae Models With Measure-ment Errors In Covariates: An Application To Sample Surveys And BigData Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Rao J. N. K. Inferential Issues In Model-Based Small Area Estimation: SomeNew Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Roszka Wojciech Creating Small Area Spatial Microdata For MultidimensionalLabor Market Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5


Salvatore Renato, Pagliarella Maria Chiara Spatio-Temporal Time-VaryingEffects Models And State-Space Models With Spatial Structure: An As-sessment Of Their Efficiency In Small Area Estimation . . . . . . . . . . 75

Singh Trijya Estimation Of Rates And Proportions For Small Areas WhenCovariates Are Measured With Error . . . . . . . . . . . . . . . . . . . . 77

Steorts Rebecca C. Constrained Smoothed Bayesian Estimation . . . . . . . . 78Sugasawa Shonosuke, Kubokawa Tatsuya Estimation And Prediction In-

tervals In Transformed Linear Mixed Models . . . . . . . . . . . . . . . . 78Sumonkanti Das Back Transformation Bias In Poverty Mapping . . . . . . . . 79Szymkowiak Marcin, Wawrowski Łukasz, Młodak Andrzej Mapping

Poverty At The Level Of Subregions In Poland Using Indirect Estima-tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Tran Bac, Lahiri Partha An Evaluation Of Design-Based Properties Of Dif-ferent Small Area Estimators Using Data From The U.S. Annual SurveyOf Public Employment And Payroll . . . . . . . . . . . . . . . . . . . . . 82

Ugarte Marıa Dolores, Adın Aritz, Goicoa Tomas, Militino Ana Fernandez,López-Abente Gonzalo Space-Time Analysis Of Young People BrainCancer Mortality In Spanish Provinces . . . . . . . . . . . . . . . . . . . 83

van den Brakel Jan, Krieg Sabine Small Area Estimation With State-SpaceCommon Factor Models For Rotating Panels . . . . . . . . . . . . . . . . 84

van der Weide Roy Estimation Of Normal Mixtures In A Nested Error ModelWith An Application To Small Area Estimation Of Poverty And Inequality 85

Warnholz Sebastian, Schmid Timo, Tzavidis Nikos Robust Fay HerriotEstimators In Small Area Estimation . . . . . . . . . . . . . . . . . . . . 86

Wawrowski Łukasz Estimation Of Poverty Headcount Ratio At Lau 1 LevelIn Poland Using Fay-Herriot Model . . . . . . . . . . . . . . . . . . . . . 87

Weidenhammer Beate, Tzavidis Nikos, Schmid Timo, Salvati NicolaDomain Prediction For Counts Using Microsimulation Via Quantiles . . 88

Wieczorek Jerzy, Pane Michael, Steorts Rebecca C. Struggles In SmallArea Estimation: Benchmarking And Weighting . . . . . . . . . . . . . . 89

Wilak Kamil Trend Estimation In Labour Force Survey In Poland . . . . . . . 89Williamson Paul, Morrissey Karyn, Espuny-Pujol Ferran Survey Reweight-

ing As A Means To Sae . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Wywiał Janusz On Sampling Design Proportional To Function Of Auxiliary

Variable Order Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Yavuz Turac, Kocak, N. Alpay, Uslu Enes E. Small Area Estimation With

Data Mining Techniques: A Case Study For Turkey . . . . . . . . . . . . 92Zhang Junni, Bryant John Full Bayesian Benchmarking Of Small Area Es-

timation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Zhang Li-Chun, Whitworth Alison Benchmarked Synthetic Small Area Es-

timation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94Zhang Li-Chun Census And Sae: Population Size Estimation . . . . . . . . . 95Żądło Tomasz On Measuring Prediction Accuracy In Small Area Estimation

In The Multivariate Case . . . . . . . . . . . . . . . . . . . . . . . . . . 95Contact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96List of Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

6


The Steering Board of the Conference

Ray Chambers, University of Wollongong

Elżbieta Gołata, Poznan University of Economics

Partha Lahiri, University of Maryland

Domingo Morales, Miguel Hernandez University Of Elche

Danny Pfeffermann, Hebrew University of Jerusalem

The Programme Committee of the Conference

Domingo Morales, Miguel Hernandez University Of Elche - Chairperson

Ray Chambers, University of Wollongong

Grażyna Dehnel, Poznan University of Economics

Eugeniusz Gatnar, National Bank of Poland, University of Economics in Katowice

Elżbieta Gołata, Poznan University of Economics

Malay Gosh, University of Florida

Jan Kordos, Central Statistical Office in Poland

Partha Lahiri, University of Maryland

Risto Lehtonen, University of Helsinki

Isabel Molina, Charles III University Of Madrid

Ralf Munnich, University of Trier

Jan Paradysz, Poznan University of Economics

Danny Pfeffermann, Hebrew University of Jerusalem

J.N.K. Rao, Carleton University

Li-Chun Zhang, University of Southampton

The Organizing Committee of the Conference

Marcin Szymkowiak, Poznan University of Economics - Chairperson

Wojciech Adamczewski, Central Statistical Office in Poland

Katarzyna Cichońska, Central Statistical Office in Poland

Tomasz Józefowski, Statistical Office in Poznan

Tomasz Klimanek, Statistical Office in Poznan, Poznan University of Economics

Jacek Kowalewski, Statistical Office in Poznan

7


The Programme of Small Area Estimation 2014 at a glance

All the technical talks, the poster session and the Welcome Party will be held in buildingA, University of Economics, al. Niepodleglosci 10. The conference banquet will be held inthe Wasowo Palace situated 50 km from Poznan. The workshop will be held in building B,University of Economics, al. Niepodleglosci 12. Lunches will be held in Collegium Historicum,ul. Święty Marcin 78. Maps with directions to the conference venues are shown on the secondto last page of the Book of Abstracts.

Room 311 Room 407 Room 408

Day

1-

Wed

nesd

ay

8:00-9:00 Registration/Reception (ground floor)9:00-9:45 Welcome Session (Aula - second floor)9:45-10:50 Plenary session (Aula - second floor)10:50-11:20 Coffee Break (second floor)

11:20-13:00Small Area Methodsfor Repeated Surveys

– –

13:00-14:30 Lunch (Collegium Historicum, ul. Święty Marcin 78 )

14:30-16:10SAE: robust andnon-parametric

methods– –

16:10-16:40 Coffee Break (second floor)

16:40-18:20SAE in poverty

mapping– –

18:30-19:15 Poster session (second floor)19:30-20:00 Concert (Aula - second floor)20:00-21:30 Welcome Party (room 111 – first floor)

Day

2-

Thu

rsda

y

9:00-10:50SAE models: selection

and checking– –


11:20-13:00SAE in Official

StatisticsTeaching SAE –

13:00-14:30 Lunch (Collegium Historicum, ul. Święty Marcin 78 )14:30-15:35 Plenary session – –15:35-16:00 Coffee Break (second floor)16:00-17:40 Contributed session 1 Contributed session 2 Contributed session 317:45 Bus to Wąsowo19:30-24:00 Conference Banquet – The Hardt Palace in Wąsowo

Day

3-

Frid

ay

9:00-10:40 SAE applicationsBenchmarking, designissues and nonresponse

in SAE–


11:10-12:50Population census and

SAEOther topics related to

SAE–

13:00-14:30 Lunch (Collegium Historicum, ul. Święty Marcin 78 )14:30-15:30 Panel discussion (Aula – second floor)15:30-16:00 Coffee Break (second floor)16:00-17:10 Contributed session 4 Contributed session 5 Contributed session 617:15-17:45 Closing ceremony (room 311 )

8


The Programme of Small Area Estimation 2014 in detail

Tuesday September 2, 2014 (building B, room 24)

The workshop: Small Area Estimation in R (Li-Chun Zhang)

9:30 - 11:00 - Small Area Estimation in R (part 1)

11:00 - 11:30 - Coffee Break


13:00 - 14:30 - Lunch (Collegium Historicum, ul. Święty Marcin 78)


16:00 - 16:30 - Coffee Break


Wednesday September 3, 2014 (building A)8:00 - 9:00 – Registration/Reception (ground floor)9:00 - 9:45 – Welcome Session (Aula - second floor)

• Rector of the Poznan Univeristy of Economics, Professor Marian Gorynia

• President of the Central Statistical Office, Professor Janusz Witkowski

• Head of Department of Statistics, Poznan Univeristy of Economics, Pro-fessor Elżbieta Gołata

• Central Statistical Office of Poland, Professor Jan Kordos

• Department of Statistics, Poznan University of Economics, Dr MarcinSzymkowiak

9:45 - 10:50 Plenary session (Aula - second floor)

• 9:45 - 10:40 J.N.K. Rao - Inferential issues in model-based small areaestimation: some new developments

• 10:40 - 10:50 Discussion

10:50 - 11:20 - Coffee Break (second floor)

11:20 - 13:00 Invited session - Small Area Methods for Repeated Surveys(room 311) organized by Partha Lahiri (chairman Wayne Fuller)

9


• 11:20 - 11:50 Partha Lahiri - An Overview of Small Area Estimation withRepeated Survey Data

• 11:50 - 12:10 Jan A. van den Brakel, Sabine Krieg - Small area estimationwith state-space common factor models for rotating panels

• 12:10 - 12:30 Enrico Fabrizi, Maria Rosaria Ferrante, Carlo Trivisano -Estimation of value added for firms cross-classified by region, industry andsize using repeated survey data

• 12:30 - 12:50 Carolina Franco, William R. Bell - Alternative Approachesto Borrowing Information Over Time in Small Area Estimation with Ap-plication to Data from the Census Bureau’s American Community Survey

• 12:50 - 13:00 Discussion


14:30 - 16:10 Invited session - SAE: robust and non-parametric methods (room311) organized by Raymond Chambers (chairman Graham Kalton)

• 14:30 - 15:00 Raymond Chambers - Two Recent Developments in Robustand Semiparametric Small Area Estimation

• 15:00 - 15:20 Beate Weidenhammer, Nikos Tzavidis, Timo Schmid, NicolaSalvati - Domain Prediction for Counts using Microsimulation via Quan-tiles

• 15:20 - 15:40 Payam Mokhtarian - On Outlier Robust Small Area Pre-diction of the Empirical Distribution Function

• 15:40 - 16:00 Forough Karlberg - Small Area Prediction for Skewed Datain the Presence of Zeroes

• 16:00 - 16:10 Discussion


16:40 - 18:20 Invited session - SAE in poverty mapping (room 311) organizedby Isabel Molina (chairman Monica Pratesi)

• 16:40 - 17:10 Isabel Molina, J.N.K. Rao - An overview of small areaestimation methods for poverty mapping

10


• 17:10 - 17:30 Gauri Datta, Abhyuday Mandal - Small area estimationwith uncertain random effects

• 17:30 - 17:50 Domingo Morales - Partitioned area-level time models forestimating poverty indicators

• 17:50 - 18:10 Roy Van der Weide - Estimation of Normal Mixtures ina Nested Error Model with an Application to Small Area Estimation ofPoverty and Inequality

• 18:10 - 18:20 Discussion

18:30 - 19:15 - Poster session (second floor)

• Maria Dolores Esteban, Domingo Morales, Augustın Perez - Area-leveltime models in R

• Tomasz Józefowski - Applying indirect estimation to special economiczones

• Anna Małasiewicz - Estimation of education level in 2011 National Censusof Population and Housing in Poland using small area estimation

• Magdalena Okupniak - Mincer model in small area statistics

• Jan Paradysz, Karolina Paradysz - Indirect estimation of disability on thebase of Polish National Census 2011

• Alessandra Petrucci, Stefano Marchetti, Caterina Giusti, Nicola Salvati -Geographic information in area level models for small area estimation

• Alina Szkop - Using small area estimators to estimate the number of dis-abled people

• Sebastian Warnholz, Timo Schmid, Nikos Tzavidis - Robust Fay HerriotEstimators in Small Area Estimation

• Łukasz Wawrowski - Estimation of poverty headcount ratio at LAU 1 levelin Poland using Fay-Herriot model

• Kamil Wilak - Trend Estimation in Labour Force Survey in Poland

19:30 - 20:00 - Concert (Aula - second floor)20:00 - 21:30 - Welcome Party (room 111 – first floor)

11


Thursday September 4, 2014 (building A)

9:00 - 10:50 Invited session - SAE models: selection and checking (room 311)organized by Danny Pfeffermann (chairman Elżbieta Gołata)

• 9:00 - 9:40 Danny Pfeffermann - Model Selection and Checking for SmallArea Estimation (30 minutes), Graham Kalton – discussant (10 minutes)

• 9:40 - 10:00 Jay Breidt, Daniel Hernandez-Stumpfhauser, Jean D. Op-somer - Variational Approximations for Selecting Hierarchical Models ofCircular Data in a Small Area Estimation Application

• 10.00 - 10:20 Jiraphan Suntornchost, Partha Lahiri - Variable selectionfor Linear Mixed Models with Applications in Small Area Estimation

• 10:20 - 10:40 Yahia El Horbaty - A Simple Score Test for Random Effectswith Application to Small Area Models

• 10:40 - 10:50 Discussion

10.50 - 11:20 - Coffee Break (second floor)

11:20 - 13:00 Invited session - SAE in Official Statistics (room 311) organizedby Jan Kordos (Parallel session) (chairman William Bell)

• 11:20 - 11:50 Jan Kordos - Small area estimation in official statistics andstatistical thinking

• 11:50 - 12:10 Danute Krapavickaite, Tomas Rudys - Application of smallarea estimation methods for Lithuanian Labour force survey data

• 12:10 - 12:30 Jan Paradysz, Karolina Paradysz - Indirect estimation ofdisability on the base of Polish National Census 2011

• 12.30 - 12:50 Jan Kubacki, Alina Jędrzejczak - Small area estimationunder spatial SAR model

• 12.50 - 13:00 Discussion

11:20 - 13:00 Invited session - Teaching SAE (room 407) organized by RistoLehtonen (Parallel session) (chairman Gauri Datta)

• 11:20 - 11:50 Risto Lehtonen - Experiences and challenges in teachingsmall area estimation

12


• 11:50 - 12:10 Jan Pablo Burgard, Ralf Munnich - SAE teaching usingsimulations

• 12:10 - 12:30 Elżbieta Gołata, Tomasz Klimanek - Challenges facingacademics and the NSI in SAE education

• 12:30 - 12:50 Esther Lopez Vizcaino Lombardıa Cortina, M. Jose, DomingoMorales - mme: An R package for small area estimation with multinomialmixed models

• 12:50 - 13:00 Discussion


14:30 - 15:35 Plenary session (room 311)

• 14:30 - 15:25 Malay Ghosh - Benchmarked Empirical Bayes Estimatorsfor Multiplicative Area Level Models

• 15.25 - 15:35 Discussion

15:35 - 16:00 - Coffee Break (second floor)16:00 - 17:40 Contributed session 1 (room 311) (chairman Wojciech Roszka)

• 16:00 - 16:15 Charlotte Articus, Jan Pablo Burgard, Ralf Munnich - AFinite Mixture Fay-Herriot Model for the Estimation of Regional RentalPrices

• 16:15 - 16:30 Yuki Kawakubo, Tatsuya Kubokawa - Modified conditionalAIC in linear mixed models

• 16:30 - 16:45 Shonosuke Sugasawa, Tatsuya Kubokawa - Estimation andPrediction Intervals in Transformed Linear Mixed Models

• 16:45 - 17:00 Bac Tran, Partha Lahiri - An Evaluation of Design-Basedproperties of Different Small Area Estimators Using Data from the U.S.Annual Survey of Public Employment and Payroll

• 17:00 - 17:15 Imanol Montoya, Aramendi, J., Garmendia, I., IztuetaA. - Mixed models for longitudinal data with applications to small areastatistics in the Basque Statistical Office

• 17:15 - 17:30 Mauno Keto, Erkki Pahkinen - On sample allocation forefficient small area estimation

13


• 17:30 - 17:40 Discussion

16:00 - 17:40 Contributed session 2 (room 407) (chairman Isabel Molina)

• 16:00 - 16:15 Stephen Haslett - The role of contextual variables in smallarea estimation of poverty

• 16:15 - 16:30 Włodzimierz Okrasa - Spatial Dynamics of CommunityWell-Being. Patterns of inequality of local deprivation, Poland 2004-2012

• 16:30 - 16:45 Łukasz Wawrowski, Marcin Szymkowiak, Andrzej Młodak– Mapping poverty at the level of subregions in Poland using indirectestimation

• 16:45 - 17:00 Sumonkanti Das - Back Transformation Bias in PovertyMapping

• 17:00 - 17:15 Enrico Fabrizi, Maria Rosaria Ferrante, Carlo Trivisano- Hierarchical Beta regression models for the estimation of poverty andinequality parameters in small areas

• 17:15 - 17:30 Trijya Singh - Estimation Of Rates And Proportions ForSmall Areas When Covariates Are Measured With Error

• 17:30 - 17:40 Discussion

16:00 - 17:40 Contributed session 3 (room 408) (chairman Jan van den Brakel)

• 16:00 - 16:15 Harm Jan Boonstra - Hierarchical Bayesian Small AreaEstimation with R

• 16:15 - 16:30 Claudia Baldermann, Timo Schmid, Nicola Salvati - Arobust unit-level model for small area estimation allowing for spatial non-stationarity

• 16:30 - 16:45 Tomas Hobza, Domingo Morales - MSM estimation andEBP in unit-level logit mixed models

• 16:45 - 17:00 Junni Zhang, John Bryant - Full Bayesian Benchmarkingof Small Area Estimation Models

• 17:00 - 17:15 Jerzy Wieczorek, Michael Pane, Rebecca C. Steorts - Strug-gles in Small Area Estimation: Benchmarking and Weighting

• 17:15 - 17:30 Bart Buelens, Jan van den Brakel - Covariate selection forsmall area estimation in repeated surveys

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• 17:30 - 17:40 Discussion

17:45 – Bus to Wąsowo19:30 - 24:00 Conference Banquet – The Hardt Palace in Wąsowo

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Friday September 5, 2014 (building A)

9:00 - 10:40 Invited session - SAE applications (room 311) organized by RalfMunnich (Parallel session) (chairman Roy van der Weide)

• 9:00 - 9:30 Ralf Munnich - Small area applications: some remarks froma design-based view

• 9:30 - 9:50 Ugarte, MD, Adın, A., Goicoa, T., Militino, A.F., López-Abente, G. - Space-time analysis of young people brain cancer mortalityin Spanish provinces

• 9:50 - 10:10 Rebecca C. Steorts - Constrained Smoothed Bayesian Esti-mation

• 10:10 - 10:30 William R. Bell, Mark Seiss - A Modeling Approach toEstimating the Mean Squared Error of Synthetic Small Area Estimators

• 10:30 - 10:40 Discussion

9:00 - 10:40 Invited session - Benchmarking, design issues and nonresponsein SAE (room 407) organized by Stefano Falorsi (Parallel session) (chairmanMichel Hidiroglou)

• 9:00 - 9:30 Andrea Fasulo, Michele D’Alo’, Lorenzo Di Biagio, StefanoFalorsi, Fabrizio Solari - Benchmark constraints for space and time unitlevel EBLUP estimators

• 9:30 - 9:50 Li-Chun Zhang, Alison Whitworth - Benchmarked syntheticsmall area estimation

• 9:50 - 10:10 Serena Arima, Gauri S. Datta, Brunero Liseo - MultivariateFay-Herriot model with structural measurement error

• 10:10 - 10:30 Janusz Wywiał - On sampling design proportional to func-tion of auxiliary variable order statistics

• 10:30 - 10:40 Discussion


11:10 - 12:50 Invited session - Population census and SAE (room 311) orga-nized by Li-Chun Zhang (Parallel session) (chairman Stephen Haslett)

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• 11:10 - 11:40 Li-Chun Zhang - Census and SAE: Population size estima-tion

• 11:40 - 12:00 Ralf Munnich - Small area estimation in the German Census2011

• 12:00 - 12:20 Paul Williamson, Karyn Morrissey, Ferran Espuny-Pujol -Survey reweighting as a means to SAE

• 12:20 - 12:40 Angela Luna-Hernandez, Li-Chun Zhang - MultivariateGeneralized Structure Preserving Estimation

• 12:40 - 12:50 Discussion

11:10 - 12:50 Invited session - Other topics related to SAE (room 407) orga-nized by Domingo Morales (Parallel session) (chairman Marıa Dolores Ugarte)

• 11:10 - 11:40 Wayne Fuller, Andreea Erciulescu - Small Area Predictionunder Alternative Model Specifications

• 11:40 - 12:00 Domingo Morales, Miguel Boubeta, Marıa Jose Lombardıa- Empirical best prediction in Poisson mixed models

• 12:00 - 12:20 M.A. Hidiroglou, Victor Estevao - A comparison of smallarea and direct estimators via simulation

• 12:20 - 12:40 Monica Pratesi, Fosca Giannotti, Caterina Giusti, StefanoMarchetti, Dino Pedreschi, Nicola Salvati - Area level SAE models withmeasurement errors in covariates: an application to sample surveys andBig data sources

• 12:40 - 12:50 Discussion

13:00 - 14:30 Lunch (Collegium Historicum, ul. Święty Marcin 78)

14:30 - 15:30 – Panel discussion (Aula – second floor) - The Newest Achieve-ments in SAE both in the Theoretical and Practical FieldOrganizer and Chair: Elżbieta GołataPanelists:

• Raymond Chambers

• Jan Kordos

• Partha Lahiri

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• Risto Lehtonen

• Isabel Molina

• Domingo Morales

• Ralf Munnich

• Danny Pfeffermann

• Li-Chun Zhang


16:00 - 17:10 Contributed session 4 (room 311) (chairman Jan Kubacki)

• 16:00 - 16:15 Renato Salvatore, Maria Chiara Pagliarella - Spatio-temporaltime-varying effects models and state-space models with spatial structure:an assessment of their efficiency in small area estimation

• 16:15 - 16:30 Tomasz Klimanek - Spatial Approach In Indirect EstimationOf Some Labor Market Characteristics On Rural Areas

• 16:30 - 16:45 Tomasz Żądło - On measuring prediction accuracy in smallarea estimation in the multivariate case

• 16:45 - 17:00 Wojciech Roszka - Creating small area spatial microdatafor multidimensional labor market analysis

• 17:00 - 17:10 Discussion

16:00 - 17:10 Contributed session 5 (room 407) (chairman Marcin Szymkowiak)

• 16:00 - 16:15 Alina Potrykowska - New pattern of migration in Poland.A migration policy perspective

• 16:15 - 16:30 Grażyna Dehnel, Jacek Kowalewski - The tax register andthe social security register in estimation methodology of short-term busi-ness statistics in Poland

• 16:30 - 16:45 Mossamet Nesa, Robert G.Clark, Carole L.Birrell - AdultsHealth Status and Behaviors in New Zealand: An Application of Multi-variate Fay-Herriot Model

• 16:45 - 17:00 Andrius Ciginas - On a concept of similarity

• 17:00 - 17:10 Discussion

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16:00 - 16:55 Contributed session 6 (room 408) (chairman Tomasz Józefowski)

• 16:00 - 16:15 Enes Ertad Eslu - Small Area Estimation With Data MiningTechniques: A Case Study For Turkey

• 16:15 - 16:30 Maciej Beręsewicz - Big data for Small Area Estimation orSmall Area Estimation for big data?

• 16:30 - 16:45 Hanna Gruchociak - Computational problems in the selec-tion of variables for multilevel models using stepwise regression

• 16:45 - 16:55 Discussion

17:15 - 17:45 Closing ceremony (room 311)

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Abstracts

Arima Serena (Sapienza University of Rome), Datta Gauri S. (U.S.Census Bureau, University of Georgia), Liseo Brunero (Sapienza Uni-versity of Rome)

MULTIVARIATE FAY-HERRIOT MODEL WITH STRUCTU-RAL MEASUREMENT ERROR

We consider a Bayesian multivariate Fay-Herriot model in which the covariatesare measured with structural measurement error. Suppose there are m smallareas of interest and let Yi denote the vector of h population characteristicsof interest in area i. Let yi be a direct estimator of Yi for area i and Xi is ap−vector of auxiliary data. We assume that the Xi’s are incorrectly recordedas Wi.

Several solutions have been proposed in small area literature addressingthe issue of the measurement error in the covariates. However, all these ap-proaches consider the auxiliary variables as independent, that is the p covari-ates X1i, ...,Xpi, recorded at the area i are assumed to be independent. Thisassumption may be largely unrealistic in those situations where the auxiliaryinformation comes from the same source and a reasonable degree of correlationamong the Xi’s is reasonable.

We propose a multivariate Fay-Herriot model with structural measurementerror and correlated auxiliary variables. The proposed model can be written asa multi-stage model:

• yi = θi + ei i = 1, . . . ,m

• θi = ν + zi′δ + Xi

′β + vi

• Wi = Xi + ui

• Xi = µ+ ti with Xi|µ,Φ ∼ Np(µ,Φ) where µ is p× 1 and Φ is p× p.

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In the above hierarchy, we have also included a q × 1 vector of covariates ziwhich are measured without error. Let Z = (z1, . . . , zm)′ be an m × q matrixof these q covariates for the m small areas, and W = (W1,W2, . . . ,Wm)′. Weassume that [X : Z] is a full column rank matrix. Moreover, ui ∼ N(0,Ci),ei ∼ N(0,Di) and vi ∼ N(0,Σ) where Ci and Di are known. Adopting theimproper noninformative prior π(ν, β, γ,Φ,Σ) = 1, we prove that the resultingposterior distribution is almost surely proper. We derive a Markov Chian MonteCarlo algorithm to obtain samples from the posterior distribution. A simulationstudy has been conducted in order to evaluate the advantages of the proposedapproach exploring different scenarios.

Small area model selection is another interesting issue recently addressed ina frequentist context. We propose an alternative Bayesian model choice andvariable selection method and investigate its properties.

Articus Charlotte (University of Trier), Munnich Ralf (University ofTrier)

AN APPLICATION OF MODEL DIAGNOSTICS FOR THE FAY-HERRIOT-MODEL IN THE CONTEXT OF ESTIMATING RE-GIONAL RENTAL PRICES

We present results of an application of model-based small area estimation (SAE)to the problem of estimating regional rental prices in Germany and discussvarious diagnostics tools for evaluating the proposed model.

In the application we estimate average rental prices on NUTS-3-level forGermany applying a Fay-Herriot model which uses socio-economic and demo-graphic indicators as well as information on the regional market situation asauxiliary variables. The analysis is based on data from the German Mikrozen-sus of 2010. Auxiliary information is obtained from a broad range of regionalindicators provided by the Federal Statistical Office including results from theGerman Zensus 2011. It is shown that, compared to direct design-based esti-mation, a considerable improvement of accuracy can be achieved.

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The focus of the presentation is on the application of model diagnostics toevaluate the proposed model. We present and apply selected diagnostic toolsproposed in the SAE-literature and the broader mixed model-context. We thendiscuss the applicability of these tools in the given real-data-context as well asthe conclusions that can be drawn for model specification in the application athand.

Articus Charlotte (University of Trier), Burgard Jan Pablo (Univer-sity of Trier), Munnich Ralf (University of Trier)

A FINITE MIXTURE FAY-HERRIOT MODEL FOR THE ES-TIMATION OF REGIONAL RENTAL PRICES

In model-based Small Area Estimation an explicit statistical model is usedto enhance efficiency of estimation in case of small subsamples. This modelassumes a fixed relationship between the statistic of interest and a set of covari-ates, which is valid for all areas under consideration and can be used to stabilizeestimation. In some applications, there might, however, be different subgroupsof areas with specific data-generating processes, i.e. specific relationships be-tween response variable and auxiliary information. In this case, estimation ofa distinct model for each subgroup would be more appropriate than one modelfor all observations. If so, the definition of subgroups becomes a crucial task inthe estimation process.

We propose a Finite Mixture Fay Herriot Model to account for unobservedheterogeneity in the data. More specifically, we assume that the statistic ofinterest stems from a mixture distribution with k components. The estimationof mixing proportions, area-specific probabilities of subgroup identity and the ksets of model parameters is then performed simultaneously. The Finite MixtureFay Herriot-Estimator is then formulated as a weighted mean of predicts frommodel 1 to k, with weights given by the area-specific probabilities of subgroupidentity.

The suggested method is tested in a model-based simulation study. It is then

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applied to the problem of estimating regional rental prices on district level inGermany.

Baldermann Claudia (Free University Of Berlin), Schmid Timo (FreeUniversity Of Berlin), Salvati Nicola (University of Pisa)

A ROBUST UNIT-LEVEL MODEL FOR SMALL AREA ESTI-MATION ALLOWING FOR SPATIAL NON-STATIONARITY

In the context of small area estimation spatial information like unit-level ge-ographical locations can be used to improve the reliability of area-specific es-timates. One way to exploit geographical information is to apply the geo-graphically weighted regression (GWR) approach and allowing for spatial non-stationarity in the model parameters. Compared to linear mixed models (LMM)the GWR de nes the relationship between the dependent and independent vari-ables locally rather than globally. The geographically weighted empirical bestlinear unbiased predictor (GWEBLUP) proposed by Chandra et al. (2012) com-bines the ideas of LMMs and GWR. EBLUP approaches are generally based onnormality assumptions and thus are expected to be sensitive to outliers. Ap-plying robust estimation methods is therefore expected to yield more reliableresults.

We present an outlier-robust version of the GWEBLUP where lower weightsare given to extreme observations. The properties of the proposed estimatorwill be studied using model-based simulations. The performance will be ex-amined under di erent scenarios of spatial non-stationarity, with and withoutoutliers. In addition, MSE estimation will be discussed. Finally, the proposedmethod will be employed for estimating the average acid neutralizing capacityfor lakes in the Northeast of the USA. The results will be compared to differentestimation methods.

Key words: Robust estimation, spatial non-stationarity, small areaestimation, geographically weighted regression

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Bell William R. (U.S. Census Bureau), Seiss Mark (Dun and Brad-street)

A MODELING APPROACH TO ESTIMATING THE MEANSQUARED ERROR OF SYNTHETIC SMALL AREA ESTIMA-TORS

Synthetic small area estimators take direct survey estimates of certain quanti-ties (e.g., means or proportions) for large areas and apply them to small areasunder the assumption that these quantities are approximately constant withineach large area. Failure of this assumption leads to synthetic error, which caneasily dominate the sampling error of the synthetic estimator. Estimation ofthe mean squared error (MSE) of synthetic estimators, and not just their sam-pling variance, is thus important. Classic approaches to this problem, suchas that of Gonzalez and Waksberg (1973), treat synthetic error as a bias andproduce estimates of average MSE and average squared bias across a groupof areas, to avoid the instability inherent in estimating squared synthetic biasfor a single area. We instead view synthetic error as random with mean zeroand with some parametric variance function. From this perspective estima-tion of synthetic MSE involves a modeling problem which thus requires: (i)specification of a parametric function for the synthetic error variance, and (ii)estimation of the parameters of that function using the data. We use simulationto study this problem for some alternative simple variance functions and alter-native methods of parameter estimation. We then illustrate application of themodeling approach to a real application involving synthetic estimation of cor-rect enumerations in the 2010 U.S. census using data from a post-enumerationfollow-up survey.

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Beręsewicz Maciej (Poznan University Of Economics, Statistical Of-fice i n Poznan)

BIG DATA FOR SMALL AREA ESTIMATION OR SMALL AREAESTIMATION FOR BIG DATA?

Big Data has been recently discussed in Official Statistics in the context of newdata sources. Existing literature on this topic is devoted to different possibil-ities of usage for statistics, quality and privacy aspects as well as estimationproblems. Nonetheless, theoretical aspects were not considered widely.

In the paper issue of Big Data will be discussed in the context of smallarea estimation. Concerns on using new data sources as auxiliary variables forexisting survey research will be discussed. Models taking into account errors inauxiliary variables will be considered. Model-based estimation using new datasources will be presented and discussed. Simulation study will be conducted.

Boonstra Harm Jan (Statistics Netherlands)

HIERARCHICAL BAYESIAN SMALL AREA ESTIMATIONWITH R

R package hbsae has been developed to provide a fast way to compute hierar-chical Bayesian small area estimates and MSEs. The package supports basicarea and unit-level models, and relies on one-dimensional numerical integration.REML model fitting is also possible. However, one advantage of hierarchicalBayesian SAE is that zero estimates of the between-area variance are avoided.Package hbsae is used to estimate municipal unemployment based on the Dutch

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Labour Force Survey, an application in which zero maximum likelihood esti-mates frequently arise.

Along with the model fit, several model comparison measures are computed,including cross-validation measures and a conditional AIC. The package alsocontains a plot method to compare estimates based on di erent models, as wellas functions for aggregation and benchmarking.

Numerical integration is a fast and convenient alternative to MCMC meth-ods, viable for the basic SAE models. For more complex models and for caseswhere it is important to be able to draw from the full distribution of quantitiesof interest, an MCMC approach is more suitable. Work is in progress to extendthe package in this direction.

Boubeta Martınez Miguel (University of A Coruna), Lombardıa MarıaJose (University Of A Coruna), Morales Domingo (Miguel HernandezUniversity Of Elche)

EMPIRICAL BEST PREDICTION IN POISSON MIXED MOD-ELS

Since the beginning of the crisis, the Spanish poverty has increased in a con-siderable way in recent years, jeopardizing the coverage of the most basic needsof families and accentuating inequalities between social classes. Modeling thenumber of people who are under the poverty line by classical methodologies atlower levels of geography disaggregation than autonomous communities presentssignificant drawbacks, since direct estimates have a high error. In this work wepropose Poisson regression models as a good tool for describing and predictingthe number of people under the poverty line. This work employs area-levelPoisson mixed models and the corresponding empirical best predictors (EBP)for treating real data about poverty in Spain by provinces. The mean squarederror (MSE) is considered as accuracy measure of the EBP and two estimatorsare proposed. The first one is a plug-in estimator with bias correction to thesecond order and the alternative is based on a parametric bootstrap. Several

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simulation experiments are carried out for analyzing the behavior of the EBPand for comparing the estimators of the MSE of the EBP. Finally the developedmethodology and software are applied to the real data set of poverty in Spain.

Breidt Jay F. (Colorado State University), Hernandez-StumpfhauserDaniel (Colorado State University), Opsomer Jean D. (Colorado StateUniversity)

VARIATIONAL APPROXIMATIONS FOR SELECTING HIER-ARCHICAL MODELS OF CIRCULAR DATA IN A SMALL AREAESTIMATION APPLICATION

We consider hierarchical regression models for circular data using the projectednormal distribution, applied in the development of weights for the Access PointAngler Intercept Survey, a recreational angling survey conducted by the US Na-tional Marine Fisheries Service. Weighted estimates of recreational fish catchare used in stock assessments and fisheries regulation. Construction of the sur-vey weights requires the distribution of daily departure times of anglers fromfishing sites, within spatio-temporal domains subdivided by mode of fishing.Because many of these domains have small sample sizes, small area estimationmethods are developed. Bayesian inference for the circular distributions onthe 24-hour clock is conducted, based on a large set of observed daily depar-ture times from another National Marine Fisheries Service study, the CoastalHousehold Telephone Survey. A novel variational/Laplace approximation tothe posterior distribution allows fast comparison of a large number of modelsin this context, by dramatically speeding up computations relative to a fastMarkov Chain Monte Carlo method while giving virtually identical results.

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Buelens Bart (Statistics Netherlands), van den Brakel Jan (Univer-sity of Maastricht, Statistics Netherlands)

COVARIATE SELECTION FOR SMALL AREA ESTIMATIONIN REPEATED SURVEYS

When many register variables are available as potential covariates in small areamodels, model selection principles can be employed to select optimal subsets ofcovariates according to some criteria, e.g. AIC. In repeated surveys, the set ofoptimal covariates may vary between the different editions of the survey. Choos-ing different sets each time has the advantage of each edition being optimal.Large variations between the selected models may render time series of outcomevariables unstable and may be an indication that the models overfit the data.Temporal comparability is improved using the same model in every edition, withthe same set of covariates. Such models are less prone to overfitting. In thispaper, we suggest approaches to select models that are constant between thesubsequent editions of a survey and we discuss advantages and disadvantagesof using such models for small area estimates in repeated surveys. We illustratethis study with small area estimates of four years of crime victimization statis-tics in the Netherlands, from 2008 through 2011. Small area estimates of 15variables from the annual survey are produced for 418 municipalities. We usearea level models with covariates selected from a set of 21 candidates includ-ing demographics, socio-economic characteristics, and crimes registered by thepolice. Typical models vary between years and contain between two and sixcovariates selected from the 21 candidate variables. The model selection crite-ria that is used – conditional AIC or leave-one-out cross validation – influencesthe outcome of the model selection procedure. We propose model selectionmethods that result in the same set of covariates for each of the four editionsof the survey. We compare estimates and MSE estimates of results obtained bydifferent models. In our case study, suboptimal but constant models result inonly slightly higher MSE estimates than optimal, annually adjusted models.

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Burgard Jan Pablo (University of Trier), Munnich Ralf (Universityof Trier)

TEACHING SAE USING SIMULATION

In small area statistics, parameter estimates generally are derived using asymp-totic. Since in practice sample sizes are often considerably small, small sampleproperties may occur to be more important than asymptotic properties, in con-trast to other fields of applications. To better understand the possible behaviorof small area estimators in practice, simulations are a feasible way for testingthe properties of the estimators of interest assuming a reasonable data set isavailable. It is important to conduct such simulation studies in a reproducibleand fair manner for enabling the derivation of proper conclusions.

In this talk first an overview over possible simulation settings for small areaestimation will be presented. On basis of this, the simulations are used toexplain in which situation which small area estimators perform well or not sowell. Using this approach the students learn to look at the structure of the datafor choosing a small area estimator.

Chambers Raymond (University of Wollongong)

TWO RECENT DEVELOPMENTS IN ROBUST AND SEMI-PARAMETRIC SMALL AREA ESTIMATION

In this presentation I will describe two new developments in modelling for smallarea estimation. The first involves the spatial extension of recently publishedresults on robust bias correction when there are asymmetric unit level and area

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level outliers in the survey data used to predict a small area mean. This work isbased on a SAR extension of the usual two level mixed model, and develops therobust bias correction idea when asymmetric outliers are present in the result-ing spatially correlated data. The second involves the extension of M-quantilemodelling for small area estimation to where the data are counts, rather thanrealisations of continuously distributed variables. M-quantile models representa robust alternative to mixed models for small area estimation, and in thiscontext we describe how this approach can be extended to situations where anon-robust generalised linear mixed model might otherwise be fitted.

Ciginas Andrius (Vilnius University)

ON A CONCEPT OF SIMILARITY

We consider a generalization of a composite estimator where the mean of thesmall area is estimated by a linear combination of the means of all areas. Itis well known how to get optimal coefficients for this linear combination, butit is difficult to estimate them. We propose a new method to evaluate thecoefficients, which is not connected to optimizations directly, but it is efficientand universal in applications.

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Datta Gauri Sankar (University of Georgia, U.S. Census Bureau),Mandal Abhyuday (U.S. Census Bureau)

SMALL AREA ESTIMATION WITH UNCERTAIN RANDOMEFFECTS

Random effects models play an important role in model-based small area esti-mation. Random effects account for any lack of fit of a regression model for thepopulation means of small areas on a set of explanatory variables. In a recentpaper, Datta, Hall and Mandal (2011, J. Amer. Statist. Assoc.) showed that ifthe random effects to account for a lack of fit of a regression model can be dis-pensed with through a statistical test, then the model parameters and the smallarea means can be estimated with substantially higher accuracy. The work ofDatta et al. (2011) is most useful when the number of small areas, m, is moder-ately large. For large m, the null hypothesis of no random effects will likely berejected. Rejection of null hypothesis is usually caused by a few large residualssignifying a departure of the direct estimator (Yi) from the synthetic regressionestimator. As a flexible alternative to the Fay-Herriot random effects model andthe approach in Datta et al. (2011), in this paper we consider a mixture modelfor random effects. It is reasonably expected that small areas with populationmeans explained adequately by covariates have little model error, and the otherareas with means not adequately explained by covariates will require a randomcomponent added to the regression model. This model is a flexible alternativeto the usual random effects model and the data determine the extent of lackof fit of the regression model for a particular small area, and include a randomeffect if needed. Unlike the Datta et al. (2011) approach which recommendsexcluding random effects from all small areas if a test of null hypothesis of norandom effects is not rejected, the present model is less restrictive. We used thismixture model to estimate poverty ratios for 5- to 17-year old related childrenfor the 50 U.S. states and Washington, DC. This application is motivated bythe SAIPE project of the US Census Bureau. We empirically evaluated theaccuracy of the direct estimates and the estimates obtained from our mixture

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model and the Fay-Herriot random effects model. These empirical evaluationsand a simulation study, in conjunction with a measure of uncertainty of thenew estimates show that they are more accurate than the frequentist and theBayes estimates resulting from the standard Fay-Herriot model.

Key words: Empirical best linear unbiased prediction, Fay-Herriotmodel, finite mixture models, hierarchical Bayes, mixed effects, SAIPEproject

Dehnel Grażyna (Poznan University of Economics), Kowalewski Jacek(Statistical Office in Poznan)

THE TAX REGISTER AND THE SOCIAL SECURITY REGIS-TER IN ESTIMATION METHODOLOGY OF SHORT-TERM BUSI-NESS STATISTICS IN POLAND

A market economy generates a demand for various data about local economic,social or environmental conditions. To meet this demand the business statis-tics information system has to be modified. One of the major factors thatrestrict the scope of changes is the cost of statistical surveys. The need for asubstantial reduction in sample sizes and accurate estimates for small domainshas highlighted the importance of increasing public statistics’ reliance on auxil-iary information from administrative registers. The study is aimed at assessingthe usefulness of available administrative sources for short-term statistics withrespect to enterprises employing over 9 people.

The paper presents major issues involved in estimating information abouteconomic activity across domains e.g. incompleteness of registers, problems ofdata linkage, non-homogenous distributions.

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El-Horbaty Yahia (University of Southampton)

A SIMPLE SCORE TEST FOR RANDOM EFFECTS WITH AP-PLICATION TO SMALL AREA MODELS

Random effects are often incorporated to account for unobservable domain-specific characteristics in a small area model. However, the presence of theseeffects in the model has some undesirable implications such as increasing thevariability of point and interval estimators and numerical difficulties in the com-putation of the small area estimators and the estimation of the prediction MSE.Thus, testing for the need of random effects is very important as it can facili-tate the computation and improve the inference. In my presentation I propose asimple score test for the need to include random effects in the small area model,with application to the linear area level and unit level models, and the mixed-logistic regression model. The random effects are treated as fixed unknowncoefficients under the alternative hypothesis and hence the implementation ofthe test does not require any distributional assumptions about them. A simu-lation study is performed to evaluate the size and power of the proposed test.Application to a real data set is also considered.

Esteban Maria Dolores (Miguel Hernandez University Of Elche),Morales Domingo (Miguel Hernandez University Of Elche), PerezAgustın (Miguel Hernandez University Of Elche)

AREA-LEVEL TIME MODELS IN R

The poster presents several area-level models with time correlated random ef-

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fects. It contains information about the restricted maximum likelihood (REML)estimation method, the empirical best linear unbiased predictors (EBLUP) andthe mean squared error (MSE) estimation. The poster describes R codes forapplying REML estimation and for calculating EBP and MSE in the introducedarea-level models. It also gives information about the R package SAERY. Thispackage contains the programmed codes in an easy-to-apply form.

Fabrizi Enrico (Catholic University of the Sacred Heart), FerranteMaria Rosaria (University of Bologna), Trivisano Carlo (Universityof Bologna)

ESTIMATION OF VALUE ADDED FOR FIRMS CROSS-CLASSI-FIED BY REGION, INDUSTRY AND SIZE FROM A REPEATEDSURVEY

We aim at the estimation of Value Added for subsets of the population of Italiansmall and medium sized manufacturing firms classified according to geograph-ical region, industrial sector and firms size. This disaggregation is needed inregional comparisons in order to avoid the confounding effect of sectorial andfirm size composition of a region’s manufacturing industry. We use data onthe Small and Medium Enterprises sample survey conducted by the Italian Na-tional Statistical Institute. The survey is conducted once a year to provide dataneeded in the estimation of aggregates for the National Account system. In thisapplication we consider data from several waves of the survey. We compare dif-ferent longitudinal small area models taking into account the peculiarities ofbusiness survey data. Considering the longitudinal dimension is natural in thiscontext as auto-correlation induced by the business cycle may be exploited toimprove the precision of estimators of business parameters in small domains.The target variable we consider, value added is positively skewed and so is thesampling distribution of total estimators in small samples. For this reason weassume log-normality of direct estimators of total value added. We adopt aBayesian approach to inference and consider linear mixed models on the log

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scale. These models are characterized by the presence of several random ef-fects, including a time effect describing year to year variation unaccounted forby the covariates. Some of these random effects are not exchangeable: e.g. timeeffects will have a dependence structure such as the random walk we considerin our application. As a consequence the model includes several variance com-ponents; some are conditional variances, while other are “marginal” variances.Reference “the same for all variances” priors for variance components can bein sharp contrast with the data (and the actual relative sizes of the randomeffects) and have an impact on posteriors of small area parameters even if theset of small area is large and the covariate works reasonably well. This is evenmore the case for a linear mixed model specified on the log-scale which is small.

Fabrizi Enrico (DISES, Catholic University of the Sacred Heart), Fer-rante Maria Rosaria (University of Bologna), Trivisano Carlo (Uni-versity of Bologna)

HIERARCHICAL BETA REGRESSION MODELS FOR THE ES-TIMATION OF POVERTY AND INEQUALITY PARAMETERSIN SMALL AREAS

Many parameters that describe poverty, social exclusion and inequality can takevalues in the (0,1) interval. This class includes headcount ratios such as theat-risk-of-poverty or material deprivation rates, the median poverty gap andthe Gini inequality index, just to cite a few.

Social scientists and policy planners often need estimates of this type ofparameters for small subpopulations for which only small or no samples areavailable. In this work, we discuss area level models for the estimation of theseparameters.

Given their nature, Beta regression models are suitable, as the Beta distribu-tion is very flexible over the (0,1) range and it allows for asymmetric samplingdistributions. We adopt a Bayesian approach with approximate inference forrelevant posterior distributions relying on MCMC algorithms. We focus on

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specific problems that we think are particularly relevant for small area appliedresearchers and discuss them with an application to real data.

The problems we consider are: i) the estimation of the at-risk-of-povertyrate; ii) the joint estimation of the material deprivation and severe materialdeprivation rates (i.e. two rates based on increasing thresholds); iii) the jointestimation of two correlated parameters, namely the at-risk-of-poverty rate andthe Gini inequality index.

The data set that we analyze is a subset of the Italian sample of the EU-SILC survey, and the small areas we target are Italian health districts, whoseadministrations play an important role in the implementation of many socialand health expenditure programs related to the contrast of poverty and socialexclusion.

When estimating the at-risk-of-poverty rate we face the problem of areas withno poors in the sample that leads us to consider zero-mixture Beta regressions,a class of models that will be extended to the multivariate setting.

In fact, the joint estimation of parameters in the (0,1) range requires multi-variate extensions of the Beta regressions: for material deprivation and severedeprivation rates, based on increasing thresholds, we discuss a multivariatelogistic-normal model for the expected values of the Beta distributions, whilefor the joint estimation of the at-risk-of-poverty rate and the Gini inequalityindex, we model the correlation between direct estimators using copula func-tions.

Fasulo Andrea (ISTAT), D’Alo’ Michele (ISTAT), Di Biagio Lorenzo(ISTAT), Falorsi Stefano (ISTAT), Solari Fabrizio (ISTAT)

BENCHMARK CONSTRAINTS FOR SPACE AND TIME UNITLEVEL EBLUP ESTIMATORS

Small area estimation techniques are becoming more and more important for theproduction of social statistics referred to sub-populations for which direct esti-mates are usually unreliable, being accompanied with standard errors. While

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most of the development in small area estimation has focused on cross-sectionaldata, many important large scale surveys are repeated in nature. Therefore, it isimportant to borrow strength not only across areas, but also from past surveys.Small area estimation methods that combine cross sectional and time seriesdata have been proposed, among others, in Pfeffermann and Burck (1990), Raoand Yu (1994), Ghosh et al. (1996), Datta et al. (1999).

Standard small area models generally consider only iid area random effects,while more realistic and efficient models should consider further random effectsrelated to meaningful components, such as iid and/or autoregressive time ran-dom effects for repeated surveys. In this paper we focus on two way linearmixed models with additive area and time random effects, to allow spatial andtime autocorrelation structures of random effects. These represent a generaltheoretical framework allowing to add more iid and/or autocorrelated randomeffects into small area estimation models (see Chambers and Saei, 2003).

Furthermore we consider small area estimators based on unit level nonpara-metric mixed models with area random effects, proposed in Opsomer et al.(2008). The nonparametric component is used to model the time effect withoutassuming a prespecified functional form using penalized splines and exploit-ing their close connections with linear mixed models. Both parametric and nonparametric unit level mixed model become cumbersome when the number of ob-servations increases. This issue is particularly relevant in social surveys, wherethe number of individuals surveyed may be very large, and becomes even morerelevant when pooling information over time from several waves of repeatedsurveys. Examples of large scale repeated surveys are the Italian Labour ForceSurvey (LFS) and EUSILC, the U.S. Current Population Survey and the Cana-dian LFS. Within this context the overall sample size may be very large, e.g.in case of the Italian LFS, each quarter a sample of almost 160.000 respondentindividuals is observed. Then modelling all the quarters from January 2004(date of the last redesign of the survey) to the end of 2013, the total numberof individual records is equal to 6.4 million. SAE methods for large scale sur-veys need to deal the case of general sampling designs with unequal samplingweights together with the need to assuring benchmark consistency with directestimates referred to major domains. In the case of repeated surveys temporalconstraints need to be solved too. Some popular cross-sectional estimators, likeFay and Herriot and pseudo EBLUP estimator, deal with the problem of generalsampling designs (the last one assures benchmarking cross-sectional propertiestoo).

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In this paper the benchmark problem for SAE estimates is coherently ex-tended to the case of space and time benchmark constraints. In particular theprocedure proposed in Di Fonso and Marini (2011) has been developed to obtaincoherent estimates for both temporal and contemporaneous constraints. This isdone in such a way that the temporal profiles of the original series is preservedat best (the movement preservation principle). A block- wise matrix notation isused to derive a useful new formulation of EBLUP unit level estimators, whichresults to be particularly effective when computation is done on a large amountof data. This work builds on those by D’Alo’ et al. (2006, 2007, 2013, 2014a,2014b)in which this approach is used to derive EBLUP unit level estimators forlinear mixed models with area effects, and with area and time effects. Finallytwo empirical studies are presented. The first study is based on synthetic datageneration finalized to show the computational performances of the method.While the second one considers the Italian Labour Force Survey (LFS) data.

BIBLIOGRAPHY

1. D’Alo’ M., Falorsi, S., Solari, F. (2006) A computationally more efficientreformulation of small area estimators based on linear mixed models, inMetodi Statistici di Integrazione di Dati da Fonti Diverse, Liseo, B., Mon-tanari, G.E., Torelli, N. Eds., Franco Angeli, Milano.

2. D’Alo’ M., Falorsi S., Solari F. (2007). Linear mixed models for generalisedrandom effects structures for small area estimation, in Proceedings of theSAE Conference IASS Satellite Meeting on Small Area Estimation, Pisa,3-5 Sept. 2007.

3. D’Alo’ M., Falorsi S., Ranalli M.G., Solari F. (2013). Computationallyfeasible non-parametric small area estimation for large data sets, ITAlianConference on Survey Methodology ITACOSM2013 , Milan, 26-28 June2013.

4. D’Alo’ M., Falorsi S., Solari F. (2014a). Generalized and efficient formula-tion of space and time unit level EBLUP for repeated surveys, submittedfor publication.

5. D’Alo’ M., Falorsi S., Loriga S. (2014b). LFS quarterly small area esti-mation of youth unemployment at provincial level, Scientific conference ofItalian Statistical Society 2014 , Cagliari, 11-13 June 2014.

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6. Datta, G.S., Lahiri, P., Maiti, T., Lu, K.L. (1999) Hierarchical Bayes es-timation of unemployment rates for the states of the U.S. Journal of theAmerican Statistical Association 94, 1074-1082.

7. Di Fonzo, T, Marini, M. (2011) Simultaneous and two-step reconciliationof systems of time series: methodological and practical issues’ - Journal ofThe Royal Statistical Society - Applied Statistics - 60, Part 2, pp. 143-164.

8. Ghosh, M., Nangia, N., Kim, D.H. (1996) Estimation of median incomeof fourperson families: A Bayesian time series approach. Journal of theAmerican Statistical Association 91, 1423-1431.

9. Opsomer, J.D., Claeskens, G., Ranalli, M.G., Kauermann, G., Breidt, F.J.(2008) Non-parametric small area estimation using penalized spline regres-sion, Journal of the Royal Statistical Society, Series B 70, 265-286.

10. Pfeffermann, D., Burck, L. (1990) Robust small area estimation combiningtime series and cross-sectional data. Survey Methodology 16, 217-237.

11. Rao, J.N.K., Yu, M. (1994) Small area estimation by combining time seriesand cross- sectional data. Canadian Journal of Statistics 22, 511-528.

Franco Carolina (U.S. Census Bureau), Bell William R. (U.S. CensusBureau)

ALTERNATIVE APPROACHES TO BORROWING INFORMA-TION OVER TIME IN SMALL AREA ESTIMATION WITH AP-PLICATION TO DATA FROM THE CENSUS BUREAU’S AMER-ICAN COMMUNITY SURVEY

Small area estimation typically seeks to improve direct survey estimates byborrowing information across the areas or from covariate data. For repeatedsurveys, one can also borrow information over time, i.e., from past survey es-timates. In doing so one question that arises is how much past data should

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be incorporated into the model? Another question that may arise is whetherpast data might be summarized for use in a model, say via an average of somenumber of previous survey estimates? We examine how the answers to thesequestions will depend generally on the specifics of a particular survey applica-tion, and then address them for the specific application of poverty estimationfor U.S. counties by modeling data from the Census Bureau’s American Com-munity Survey (ACS). For this application the second question arises naturallywhen modeling ACS estimates from a single year of data collection, since theACS also regularly provides estimates for all counties based on five successiveyears of data collection.

Fuller Wayne A. (Iowa State University), Erciulescu Andreea L. (IowaState University)

SMALL AREA PREDICTION UNDER ALTERNATIVE MODELSPECIFICATIONS

Construction of small area predictors and estimation of the prediction meansquared error, given different types of auxiliary information and for differentpopulation models are illustrated. Of interest are situations where the meanand variance of an auxiliary variable are subject to estimation error. Fixed andrandom specifications for the auxiliary variables are considered. The possibleefficiency gains associated with the random specification for the auxiliary vari-able measured with error are demonstrated. Some of the techniques are appliedin a study of soil erosion.

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Ghosh Malay (University of Florida), Kubokawa Tatsuya (Universityof Tokyo), Kawakubo Yuki (University of Tokyo)

BENCHMARKED EMPIRICAL BAYES ESTIMATORS FORMULTIPLICATIVE AREA LEVEL MODELS

The paper develops empirical Bayes and benchmarked empirical Bayes esti-mators of positive small area means under multiplicative models. A simpleexample will be estimation of per capita income for small areas. It is nowwell-understood that small area estimation needs explicit, or at least implicituse of models. One potential di culty with model-based estimators is that theoverall estimator for a larger geographical area based on (weighted) sum of themodel-based estimators is not necessarily identical to the corresponding directestimator, such as the overall sample mean. One way to x such a problem is theso-called benchmarking approach which modi es the model-based estimators tomatch the aggregate direct estimator.

Benchmarked hierarchical and empirical Bayes estimators have proved to beparticularly useful in this regard. However, while estimating positive small areaparameters, the conventional squared error or weighted squared loss subject tothe usual benchmark constraint does not necessarily produce positive estima-tors. Hence, it is necessary to seek other meaningful losses to alleviate thisproblem.

In this paper, we consider the transformed Fay-Herriot model as a multiplica-tive model for estimating positive small area means, and suggest a weightedKullback-Leibler divergence as a loss function. We have found out that theresulting Bayes estimator is the posterior mean and that the correspondingbenchmarked Bayes and empirical Bayes estimators retain the positivity con-straint.

The prediction errors of the suggested empirical Bayes estimators are investi-gated asymptotically, and their second-order unbiased estimators are provided.In addition, bootstrapped estimators of these prediction errors are also pro-vided. The performance of the suggested procedures is investigated through

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simulation as well as with an empirical study.

Gołata Elżbieta (Poznan University of Economics), Klimanek Tomasz(Poznan University of Economics, Statistical Office in Poznan)

CHALLENGES FACING ACADEMICS AND THE NSI IN SAEEDUCATION

The aim of the paper is to present findings of a study focused on the experienceof teaching Small Area Estimation (SAE). The experience of teaching SAE andchallenges involved in the process are analysed from the point of view of bothacademic teachers and that of the NSI. The article discusses these issues froma wider perspective of statistical education.

The topic refers to Polish conditions, but particular issues are comparedto experience and practices from other countries. Information discussed inthe article mainly comes from a special survey conducted among employeesof the Central Statistical Office and regional statistical offices. Researchersfrom Polish universities involved in such survey have also taken part in thesurvey. The article also addresses the question of introducing SAE modules inthe EMOS project (European Master in Official Statistics). Survey data aresupplemented with information collected in the process of monitoring trainingcourses and workshops organized by leading centres specializing in SAE. Surveyresults are discussed in relation to a similar survey conducted in the Eurostatproject ESSnet on Small Area Estimation in 2010.

The survey contains questions about the level of interest in learning SAEand the need to implement SAE methodology, the range of subjects taught,the range of applications, forms of training, types of courses, software usedand teaching methods. The main objective of the survey is to determine thelevel of interest in small area estimation, evaluate the demand for practical andtheoretical knowledge in this field, and, finally, to formulate recommendationsfor universities and statistical institutes.

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Gruchociak Hanna (Poznan University of Economics)

COMPUTATIONAL PROBLEMS IN THE SELECTION OF VARI-ABLES FOR MULTILEVEL MODELS USING STEPWISE REGRES-SION

A multilevel analysis enables taking into account the diversity of the level of theanalysed variables and the character of relationships among them depending onthe allocation of the first level units to the higher-level units (groups). Further,the dividing of the studied population into groups enables the explanation of apart of the variability of the dependent variable with the use of explanatory vari-ables defined at higher levels. Its usefulness for estimating the socio-economicvariables was studied in previous papers of the author. For large populationscharacterized by multilevel structure important disadvantage of this approachis, however, a large computational complexity, and hence often unacceptablylong computation time. The main objective of this presentation is to proposesome simplification of forward stepwise multilevel regression algorithm, allow-ing reducing the time of selection of variables in the model multiple times. Theconsiderations will be illustrated by estimation of the data corresponding to thematrix of employment-related flows for the year 2011.

Key words: multilevel modeling, multilevel structure, random effects,the matrix of employment-related flows, commuter routes

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Haslett Stephen (Massey University)

THE ROLE OF CONTEXTUAL VARIABLES IN SMALL AREAESTIMATION OF POVERTY

Small area estimation (SAE) methods have been used in a wide variety of appli-cations. For what are often historical reasons, the commonly used SAE methodcan vary with the application. In 2003, Elbers, Lanjouw and Lanjouw publisheda paper in Econometrica, which has become the standard method (often calledELL) for small area estimation of poverty in the developing world. The methoduses both sample survey and census unit record data, is supported by the WorldBank’s software package PovMap, and has been key in allocation of tens of bil-lions of dollars of aid since 2003. Because ELL, like spatial microsimulation, wasdeveloped largely outside the statistical mainstream, its similarities, strengthsand weaknesses have been the topic of much debate among statisticians, espe-cially since the method could in principle be applied in a wide range of otherapplication areas. Particular recent criticisms relate to bias (because there areno area-specific predictors of random effects), and to understated estimatedstandard errors. The ELL method will be outlined briefly in concept, and itslinks to other SAE methods (in the statistical mainstream, the econometricliterature and beyond) will be considered. While other aspects will also be dis-cussed, a focus is the central role played in ELL models by choice of contextualvariables to control area-specific bias, and regression variables to reduce stan-dard errors for small area estimates. The marked reduction in standard errorsfrom ELL, when compared with some other SAE methods, reflects in part anapparent focus in the published statistical literature on predicting rather thanminimising random effects and standard errors, but also highlights the centralrole of sound and careful choice of regressors and contextual variables in anygood model, whatever the choice of SAE method.

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Hidiroglou Michel (Statistics Canada), Estevao Victor (Statistics Ca-nada)

A COMPARISON OF SMALL AREA AND DIRECT ESTIMA-TORS VIA SIMULATION

Domain estimates at Statistics Canada are typically obtained using direct esti-mators. An alternative way of producing these estimates is through small areaprocedures.

In this presentation, we compare the performance of these two approachesvia a simulation. The population is generated using a hierarchical model thatincludes both area effects and unit level random errors. The population ismade up of mutually exclusive domains of different sizes, from a small numberof units to a large number of units. We select many independent simple randomsamples of fixed size from the population and compute various estimates for eachsample using the available auxiliary information. These estimates are calculatedusing synthetic estimators (indirect estimates), calibration estimators (directestimates), and unit level based estimators (small area estimates).

The performance of these estimators is summarized based on their design-based properties.

Hobza Tomas (Czech Technical University), Morales Domingo (MiguelHernandez University Of Elche)

MSM ESTIMATION AND EBP IN UNIT-LEVEL LOGIT MIXEDMODELS

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In this contribution we deal with an empirical best predictor (EBP) of smallarea specific random effect in the context of a unit-level logistic mixed model.Behavior of the EBP based on parameter estimates obtained by the method ofsimulated moments (MSM) is studied by a Monte-Carlo simulation experimentunder different scenarios. Performance of an estimator of the mean squarederror (MSE) of EBP based on approximation of the analytic form is comparedwith performance of a bootstrap estimator.

Józefowski Tomasz (Statistical Office in Poznan)

APPLYING INDIRECT ESTIMATION TO SPECIAL ECONO-MIC ZONES

Modern users of statistical information expect up-to-date and accurate infor-mation about subpopulations defined by different variables and for different ge-ographical units. There is a growing demand for non-typical, specific domainsand functional areas, such as local labour markets, urban and cross-border ar-eas or special economic zones. Information about special economic zones is ofparticular significance to potential investors as well to state authorities. It isespecially important in the context of the recent debate concerning the futureexistence of special economic zones. The desirability of special economic zonescan, however, be only assessed given complete and reliable information aboutthem. Considering the cost of obtaining such information, the growing budgetconstraints related to statistical surveys and the desire to reduce the respondentburden resulting from statistical reporting, it is necessary to rely on alternativeestimation methods provided by small area statistics. Faced with the problemof small sample sizes for specific subdomains, these techniques can producecomprehensive information about the characteristics of special economic zonesbased on data from various sources, without the need for additional surveys,and hence extra costs.

In view of the above, the main purpose of the paper is to present the po-tential of small area statistics to supply a comprehensive description of special

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economic zones. The first part will cover the economic and legal aspects ofthe existence of special economic zones in Poland, including their benefits andthreats they pose. The second part will be devoted to key theoretical aspectsof indirect estimation and the potential use of small area statistics methods tooffer a comprehensive assessment of the desirability of special economic zones.More specifically, the author will focus on the possibility of using indirect es-timation to measure selected characteristics of the labour market in specialeconomic zones

Key words: indirect estimation, small area statistics, special eco-nomic zones

BIBLIOGRAPHY

1. Gołata E. (2004), Estymacja pośrednia bezrobocia na lokalnym rynkupracy, Wydawnictwo Akademii Ekonomicznej w Poznaniu, „Prace habili-tacyjne”, nr 11.

2. Paradysz J. (2008), Kryteria dobroci estymacji dla małych obszarów, Kon-ferencja naukowa z okazji jubileuszu 90-lecia GUS: Statystyka społeczna:dokonania — szanse — perspektywy, Kraków, 28—30 stycznia

3. Prusek A., Nelec W. (2004), Analiza oddziaływania SSE Euro-Park Mielecna rozwój społecznoekonomiczny i rynek pracy subregionu mieleckiego,Zeszyty Naukowe Akademii Ekonomicznej w Krakowie, 651, 137-155.

4. Rao J.N.K (2003), Small Area Estimation, Wiles Series in Survey Method-ology, A John Wiley & Sons, INC., Publication.

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Karlberg Forough (Luxembourg Statistical Services)

SMALL AREA PREDICTION FOR SKEWED DATA IN THEPRESENCE OF ZEROES

Skewed distributions with representative outliers pose a problem in many sur-veys. Various small area prediction approaches for skewed data based on trans-formation models have been proposed. However, in certain applications of thosepredictors, the fact that the survey data also contain a non-negligible numberof zero-valued observations is sometimes dealt with rather crudely, for instanceby arbitrarily adding a constant to each value (to allow zeroes to be consid-ered as “positive observations, only smaller”, instead of acknowledging theirqualitatively different nature).

On the other hand, while a lognormal-logistic model has been proposed (toincorporate skewed distributions as well as zeroes), that model does not includeany hierarchical aspects, and is therefore not explicitly adapted to small areaprediction.

In this paper, we consolidate the two approaches by extending one of thealready established log-transformation mixed small area prediction models toincorporate a logistic component. This allows for the simultaneous, systematic,treatment of domain effects, outliers and zero-valued observations in a sin-gle framework. We benchmark the resulting model-based predictors (againstrelevant alternatives) in applications to simulated data as well as empirical(IPUMS-CPS) income data.

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Kawakubo Yuki (University of Tokyo), Kubokawa Tatsuya (Univer-sity of Tokyo)

MODIFIED CONDITIONAL AIC IN LINEAR MIXED MOD-ELS

In linear mixed models, the conditional Akaike Information Criterion (cAIC) isa procedure for variable selection in light of the prediction of specific clustersor random effects. This is useful in problems involving prediction of randomeffects such as small area estimation, and much attention has been receivedsince suggested by Vaida and Blanchard (2005). A weak point of cAIC is thatit is derived as an unbiased estimator of conditional Akaike information (cAI)in the overspecified case, namely in the case that candidate models include thetrue model. This results in larger biases in the underspecified case that the truemodel is not included in candidate models. In this talk, we derive the modifiedcAIC (McAIC) to cover both the underspecified and overspecified cases, andinvestigate properties of McAIC. It is numerically shown that McAIC has lessbiases and less prediction errors than cAIC.

This talk is based on Kawakubo and Kubokawa (2014).

BIBLIOGRAPHY

1. Kawakubo, Y. and Kubokawa, T. (2014). Modified conditional AIC inlinear mixed models. J. Multivariate Anal. 129, 44-56

2. Vaida, F. and Blanchard, S. (2005). Conditional Akaike information formixedeffects models. Biometrika 92, 351-370.

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Keto Mauno (Mikkeli University of Applied Sciences), Pahkinen Erkki(University of Jyvaskyla)

ON SAMPLE ALLOCATION FOR EFFICIENT SMALL AREAESTIMATION

A survey research based on sampling design is a common means in producingarea statistics. It is quite conventional that different subgroups, domains orgroupings of the population which are interesting are defined after samplingprocess. The consequence of this kind of action is that area level allocation ofsampling units is coincidental, which causes problems at the calculation stage.Especially areas with low or zero sample size cause severe difficulties. Thesetypes of estimation problems have been solved by deploying auxiliary informa-tion retrieved from available records or registers and by implementing model-assisted or fully model-based estimation. Another approach is to define areas ofinterest as strata in sample design and to solve the allocation problem so that itis possible to obtain optimal results also on area level when model and estima-tion method (EBLUP in this case) are given. A proxy-y variable, which replacesquite often unknown response variable y, is used to enable two allocations, andis estimated with a regression model developed from a small pre-sample of thepopulation.

First this article includes a short description of five allocation methods knownfrom earlier literature, which take regional sample sizes into account. Those are:equal and proportional allocation, Neyman allocation (1934), power allocationof Bankier (1988) and a non-linear programming method due to Choudry et al.(2012), which is based on proxy-y. Furthermore, two new allocation solutionswhich utilize auxiliary variable, linear mixed model and EBLUP estimation areintroduced. First solution is based on the main component g1 of mean squarederror (MSE) of estimated area total and the second one SIopt makes also useof proxy-y. Empirical comparisons of performances of different area allocationsare based on estimation results of sample simulations drawn from a Finnishpublic register of block and row house apartments being for sale. This register

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contains information of 9 815 apartments located in 14 geographical areas (mosttowns) in spring 2011.

Key words: Regional statistics, Areal level sample size allocation,Model-dependent estimation, Proxy variable

Klimanek Tomasz (Poznan University of Economics, Statistical Officein Poznan)

SPATIAL APPROACH IN INDIRECT ESTIMATION OF SOMELABOR MARKET CHARACTERISTICS ON RURAL AREAS

Transformation of political system in Poland and integration process with theEuropean Union have changed the Polish reality at the turn of the twentiethand the twenty-first century. The necessity for the current, detailed and reliableinformation about many aspects of social life is systematically increasing. Thepicture of the Polish Labor Market on rural areas may not meet the recipients’needs, because the data provided by the public statistics are not detailed enoughand they are not sufficient to trace the socio-economic changes taking place inrural areas. This article presents an attempt to estimate characteristics of LaborMarket for rural areas of Wielkopolska Region (Poland), with application of thetechnique of indirect estimation with the use of spatial approach by borrowingthe strength over space.

The estimates will be enriched by the analysis of the nature of rural areasin Wielkopolska Region, deagrarianisation changes and demographic structuresat local level. The original method of territorial units classification will beproposed to accommodate the intensity and the course of deagrarianisationprocess. The estimates obtained by the use of small area statistics methodologywill be of a great importance because taking into account small sample sizesthe dedicated surveys do not allow for producing acceptable estimates at locallevel.

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Kordos Jan (Central Statistical Office of Poland, Warsaw Manage-ment Academy)

SMALL AREA ESTIMATION IN OFFICIAL STATISTICS ANDSTATISTICAL THINKING

The paper consists of three parts related to SAE in official statistics and as-sessment of data quality. The first part deals with the general mission of thenational statistics institutes to produce high quality statistical information onthe state and evolution of the population, the economy, the society and the en-vironment. These statistical results must be based on scientific principles andmethods. They must be made available to the public, politics, economy andresearch for decision-making and information purposes. There is an increas-ing users demand in terms of: (i) accuracy, (ii) timeliness, (iii) coherence, (iv)comparability.

The second part considers issues connected with applications of SAE proce-dures in official statistics in the contexts of increasing users demands for infor-mation in different breakdowns with stable or even decreasing budget while be-ing legally bound to control the response burden. The author presents generallystages of development of SAE in official statistics, starting with internationalseminars and conferences (e.g. Platek et.al, 1987; Warsaw, 1992; Riga, 1999),SAE Conferences (Finland, Jyvaskyla, 2005; Italy, Pisa, 2007; Spain, Elche2009; Germany, Trier, 2011; Thailand, Bangkok, 2013), and special projects(EURAREA, 2001-2005; SAMPLE, 2009-2011). Next applications of the SAEin official statistics in some areas and countries are discussed.

The last part is devoted to statistical thinking in the contexts of small areastatistics and TQM. After presenting philosophy of statistical thinking, andits connection with TQM, the author tries to discuss applications of SAE inofficial statistics as sets of interconnected processes, taking into account differentaspects of data quality. At the end some conclusions are given.

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Krapavickaite Danute (Vilnius Gediminas Technical University),Rudys Tomas (Vilnius University)

APPLICATION OF SMALL AREA ESTIMATION METHODSFOR LITHUANIAN LABOR FORCE SURVEY DATA

The aim of the paper is to estimate fractions of unemployed for counties usingdata of registered unemployment. The small area estimation methods are ap-plied for binary study variable of the Labor force survey. Area level and unitlevel models are applied. The main directions of small area estimation methodsdrawn in [2] are used, experience of estimation for binary study variable in [1],[4] is taken into account, LaplacesDemon computer software [3] is applied forBayesian Inference.

BIBLIOGRAPHY

1. H. J. Boonstra, B. Buelens, and M. Smeets. Estimation of municipalunemployment fractions – a simulation study comparing different smallarea estimators, Statistics Netherlands, Projectnr: DMH-205714 (2009).

2. J. N. K. Rao, Small area estimation, Hoboken: John Wiley & Sons (2005).

3. Statisticat LLC (2014). LaplacesDemon: Complete environment for BayesianInference. R package version 14.04.05, URL http://www.bayesian-inference.com/software

4. Y. You, and B. Chapman. Small area estimation using area level modelsand estimated sampling variances, Survey Methodology 32(1), (2006), 97-103.

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http://www.bayesian-inference.com/software

http://www.bayesian-inference.com/software


Kubacki Jan (Statistical Office in Lodz), Jędrzejczak Alina (Univer-sity of Lodz, Statistical Office in Lodz)

SMALL AREA ESTIMATION UNDER SPATIAL SAR MODEL

In the paper the method of small area estimation under spatial Simultane-ous Autoregressive (SAR) model is presented. The estimation was conductedby means of both spatial EBLUP and hierarchical Bayes method, using SARrandom effects that depend on a proximity matrix and a spatial autoregressioncoefficient ρ. The computation procedure applied in the paper adapted the ideaof parameter estimation for small areas using hierarchical Bayes method in thecase of known model hyperparameters presented in Kubacki (2012). As an ex-ample the data about average per capita available income from Polish HouseholdBudget Survey for counties (NUTS4) and auxiliary variables from Polish TaxRegister (POLTAX) were used together with digital maps for Polish counties.The computations performed by sae package for R-project environment and aspecial procedure prepared for WinBUGS software reveal that consistent esti-mates of model parameters can be obtained. The precision measures for directestimates were determined using three-stage methods, where BRR, bootstrapand Generalized Variance Function were applied (Kubacki, Jędrzejczak, 2012).For higher ρ values some MSE reduction was observed, what seems more evi-dent for HB-SAR method as compared with the spatial EBLUP. In our opinion,the Gibbs sampler, revealing the simultaneous nature of processes (for larger ρ)especially for random effects, can be a good starting point for the simulationsbased on stochastic SAR processes.

Key words: Small area estimation, Simultaneous Autoregressive (SAR)model, hierarchical Bayes estimation, spatial EBLUP, WinBUGS

BIBLIOGRAPHY

1. Kubacki, J., (2012) Estimation of parameters for small areas using hierar-chical Bayes method in the case of known model hyperparameters, Statis-

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tics in Transition-new series, Summer 2012, Vol. 13, No. 2, 261—278

2. Kubacki, J., Jędrzejczak A., (2012) The Comparison of Generalized Vari-ance Function with Other Methods of Precision Estimation for PolishHousehold Budget Survey, Studia Ekonomiczne, 120, 58-69

Lahiri Partha (University of Maryland), Suntornchost Jiraphan (Chu-lalongkorn University)

VARIABLE SELECTION FOR LINEAR MIXED MODELS WITHAPPLICATIONS IN SMALL AREA ESTIMATION

In small area estimation, linear mixed models are frequently used. Variableselection methods for linear mixed models are available. However, in manyapplications such as small area estimation data users often apply variable se-lection methods that ignore the random effects. In this paper, we first evaluatethe accuracy of such variable selection method for the Fay-Herriot model, aregression model when dependent variable is subject to sampling error vari-ability. We show that the approximation error, that is, the difference betweenthe standard variable selection criterion and the corresponding ideal variableselection criterion without any sampling error variability, does not convergeto zero in probability even for a large sample size. In our simulation, we no-tice that standard variable selection criterion could severely underestimate theideal variable selection criterion in presence of high measurement error vari-ability. We propose a simple adjustment to the standard variable selectionmethod for a general linear model that reduces the approximation errors. Inparticular, we show that the approximation error for our new variable selectioncriterion converges to zero in probability for large sample size. Using a MonteCarlo simulation, we demonstrate that our proposed variable selection criteriontracks the ideal variable selection criterion very well compared to the standardvariable selection method.

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Lahiri Partha (University of Maryland)

AN OVERVIEW OF SMALL AREA ESTIMATION WITH RE-PEATED SURVEY DATA

One of the key factors that lead to the success of small area methodology inpractice is the availability of strong auxiliary variables, especially those avail-able at the small area level. The grand success of the the Small Area Incomeand Poverty Estimates (SAIPE) program of the Unites States, which routinelyprovides estimates of income and poverty related statistics for different smallgeographic areas of the United States, can be largely attributed to the use ofstrong small area level auxiliary data from various administrative sources. How-ever, in many small area projects good auxiliary data are not readily available.In some cases, the use of the auxiliary data, even though available and beneficialstatistically speaking, is discouraged from certain non-statistical considerations.Surveys that are repeated over time can be extremely useful to deal with smallarea challenges in such important situations since auxiliary data can be derivedfrom past surveys. Repeated surveys not only offer opportunities for improv-ing small area statistics that are usually produced in cross-sectional surveys,they indeed provide opportunities to develop reliable estimates of changes overtime, which may be more important than estimating current time estimates insome socio-economic projects. Moreover, repeated surveys could convenientlyhelp statisticians explaining the benefits of small area statistics to public policymakers. There is no denying the fact that modeling is inevitable in small areaestimation. But, for repeated surveys, with various good opportunities for theapplication of small area estimation come complexity in modeling and differentestimation issues, which I will discuss in this overview talk.

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Lehtonen Risto (University of Helsinki)

EXPERIENCES AND CHALLENGES IN TEACHING SMALLAREA ESTIMATION

Today, small area estimation (SAE) represents a well established scientific re-search area in university statistics. Applications of SAE methods are expandingto different areas of society. SAE and related methods are increasingly used inNSIs and international and supranational organizations, and in commercial sur-vey industry. Societal impact of SAE research and application tends to grow.It can be foreseen that demands for small area statistics in different operatingareas of society continue to increase. This scenario offers a challenge not onlyto SAE research but also for SAE teaching.

A certain SAE ’ecosystem’ of more or less inter-related components hasemerged including such components as: a long series of scientific conferences onSAE (since 1970’s; the current SAE2014 Conference as a good example), text-books devoted to SAE (e.g. Jon Rao 2003; Nick Longford 2005), SAE chaptersin edited books and hundreds of journal articles, active research groups, large-scale international research projects and programs (e.g. U.S. SAIPE; EU’s FPprojects EURAREA, AMELI, SAMPLE, etc.), geocoded and spatio-temporaldatabases and ’big data’ sources, and a variety of software tools for computingand graphical illustration (capabilities of R, SAS, WinBUGS,...). Moreover,SAE related RTD appears lively in applied disciplines such as geography andgeoinformatics (GIS), economics (spatial econometrics), health sciences (diseasemapping), social and actuarial sciences (poverty and nutrition mapping; spa-tial microsimulation), bioinformatics, and agricultural and environmental sci-ences (environmetrics; crop area estimation; forestry inventories), and further,in official statistics. Activities in these areas often include teaching respectivemethods in university courses. Thus, the SAE ’ecosystem’ is expanding and be-coming more diverse. There is an obvious need for better interaction betweenthe various methodological approaches. There are various possible platformsfor interaction.

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SAE teaching, one of the components of the ’ecosystem’, might have gotless attention than the others. My impression is that regular SAE courses instatistics are given in a fairly small number of universities. The overall pictureof the state-of-the art however is incomplete and more information is needed.In some universities, SAE related courses are offered irregularly. SAE materialsare sometimes included in more general methodological courses in statistics andapplied disciplines. SAE courses are also given by various actors as intensivecourses in connection to SAE conferences and elsewhere. The level of courses(e.g. introductory, advanced; theoretical, applied) and required backgroundknowledge can vary. Considering university-level SAE teaching, students needa good enough background knowledge in statistics including for example sta-tistical inference, survey sampling, modelling, data integration, data analysisand statistical computing, making it demanding to implement a successful SAEstudy programme.

In the paper, aspects related to SAE teaching are discussed to some ex-tent in the framework sketched above. Some experiences in SAE teaching arepresented. Options for development are drafted.

Luna Angela (University of Southampton), Zhang Li-Chun (Univer-sity of Southampton)

MULTIVARIATE GENERALIZED STRUCTURE PRESERVINGESTIMATION

Small area compositions are two-way contingency tables with both row andcolumn margin known, where the rows correspond to the areas (or domains) ofinterest. Given the marginal constraints, in order to produce an estimate of acomposition only the log-linear interactions of the table need to be estimated.Purcell and Kish, (1980), propose the Structure PREserving Estimation method(SPREE), where the unknown interactions are replaced by those derived from aknown composition based on a previous census or proxy data from administra-tive sources. Provided that a sampling estimate from the target population is

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available, Zhang and Chambers (2004) propose generalized SPREE (GSPREE)models that allow for proportional adjustments of the auxiliary interactions, aswell as cell-specific random effects. They show that the structural parametersand the random effects can be recast in associated generalized linear models(GLMs) with additional nuisance parameters.

In this work, we develop multivariate GSPREE models, where the fixedeffects predictors allow for up to (J−1)×(J−1) parameters and J is the numberof columns of the table. The result is the most possible flexible structural linearrelationship between the log-linear interactions. ML estimates of the parameterscan be obtained using standard software for GLMs for Multinomial or Poissondata. For mixed-model extensions we introduce cell-specific random effectsdirectly under the Generalized Linear Mixed Models (GLMM) framework. Weshow that this simplifies the model-fitting procedure, and that the MLE of theGLMM regression coefficients can be obtained from the model without randomeffects.

Key words: Small area compositions, log-linear interaction, Multino-mial and Poisson distributions, random effects

López Vizcaıno Esther (Galician Institute Of Statistics), LombardıaCortina Marıa Jose (University Of A Coruna), Morales Domingo(Miguel Hernandez University Of Elche)

MME: AN R PACKAGE FOR SMALL AREA ESTIMATIONWITH MULTINOMIAL MIXED MODELS

The mme package for R implements multinomial area level mixed models forsmall area estimation. One of the models is based on the area level multinomialmixed model with independent random effects for each category of the responsevariable (López-Vizcaıno et al, 2013). In the rest of the models we take advan-tage from the availability of survey data from different time periods and weuse a multinomial model with independent random effects for each category of

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the response variable and with independent and correlated time and domainrandom effects.

In all the models the package use two approaches to estimate the mean squareerror (MSE), first through an analytical expression and second by bootstraptechniques.

The obtained model-based estimates for all the models are compared withthe direct ones. They have lower mean squared errors, especially for countieswith small sample size.

BIBLIOGRAPHY

1. Lopez-Vizcaıno, ME, Lombardıa, MJ and Morales, D (2013) Multinomial-based small area estimation of labour force indicators. Statistical Mod-elling. 13: 153-178.

Marchetti Stefano (University of Pisa), Giusti Caterina (University ofPisa), Salvati Nicola (University of Pisa), Petrucci Alessandra (Uni-versity of Firenze)

GEOGRAPHIC INFORMATION IN AREA LEVEL MODELSFOR SMALL AREA ESTIMATION

The aim of this work is to compare, using simulation studies, the classical Fay-Herriot model with three different extensions of it: the Semiparametric Fay-Herriot model proposed by Giusti et al. (2012), the Spatial Fay-Herriot modelproposed by Salvati (2004), Singh et al. (2005) and Petrucci & Salvati (2006),and, finally, the Spatial Nonstationary Fay- Herriot model recently proposedby Chandra et al. (2014). These models are able to take into account thespatial proximity effects between the small areas, overcoming the hypothesisof spatial uncorrelation of the Fay-Herriot model. In fact, this hypothesis isoften unrealistic in agricultural, environmental, economic and epidemiologicalapplication.

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Małasiewicz Anna (Statistical Office in Poznan)

ESTIMATION OF EDUCATION LEVEL IN 2011 NATIONALCENSUS OF POPULATION AND HOUSING IN POLAND US-ING SMALL AREA ESTIMATION

The level of education is one of the main characteristics that define the structureof the country’s population. Through comparisons of estimates the populationby level of education in consecutive censuses we can assess the evolution of thesociety. The National Census of Population and Housing in Poland, which tookplace in 2011, used a different method of data collection than ever before. Theuse of registers and information from public administration systems permittedto reduce a respondent burden and costs of the project. Some characteristicsof population was supplemented by carrying out a sample survey covering 20%of the population of the country. The methodology used in 2011 Census allowsto obtain reliable estimates at NUTS 4 level, while insufficient sample size atthe lower levels of spatial aggregation does not allow using classical methods ofestimation. Therefore, it is necessary to use a small area estimation in order toobtain estimates of specific cross-sections.

The main purpose of this article is to estimate the population by level ofeducation at NUTS 5 level in Poland. For this aim, methodological solutions ofsmall area estimation - design-based and model-assisted methods will be used.As the results information about education at a low level of spatial aggregationwill be provided and the observation of the dynamics of the level of education ofthe population in the period between censuses at NUTS 5 level will be possible.

Key words: 2011 Census in Poland, population by level of education,design–based estimation, model–assisted methods, small area estima-tion

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Mokhtarian Payam (University of Wollongong)

ON OUTLIER ROBUST SMALL AREA PREDICTION OF THEEMPIRICAL DISTRIBUTION FUNCTION

Outliers are a well-known problem when tting models to survey data. The out-lier issue is even more challenging when the aim is the prediction of small areaquantities. The main robust-projective approaches that have been developedso far for this problem have focused on modifying the parameter estimatingequations to make them less sensitive to sample outliers. A problem with therobust-projective approach is that it assumes that all non-sampled units fol-low the working model so that any deviations from this model are distributedabout zero and hence, on average, cancel out. The resulting robust estimatorsfor the population mean can be substantially biased when outliers are drawnfrom a distribution that has a di erent mean from non-sampled survey data.This naturally leads one to consider an outlier robust bias-correction for theseestimators. We propose an outlier robust-predictive approach based on thenrobust random effect block bootstrap (RREB) technique for fitting the smallarea model from a linear mixed model perspective. We also develop an out-lier robust procedure to predict the empirical distribution function of the smallarea. Monte Carlo simulation results are presented to provide some evidencefor our claim that the proposed bias-corrected RREB method is robust to theinfluence of outliers. This also leads to more reliable prediction for the smallarea empirical distribution function and more stable mean-squared error esti-mates than comparable outlier robust approaches that have been proposed inthe literature.

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Molina Isabel (Charles III University Of Madrid), Rao J.N.K. (Car-leton University)

AN OVERVIEW OF SMALL AREA ESTIMATION METHODSFOR POVERTY MAPPING

Poverty maps are aiding governments and international organizations to designmore efficient regional development policies. Unfortunately, official surveys usedto asses the living conditions of people do not usually have sufficient sample sizeto cover adequately all the target regions. The main approaches for small areaestimation techniques for poverty mapping will be reviewed, discussing theiradvantages and disadvantages. Recent variants of the basic methods will bedescribed, and results of an application with Spanish data will be shown.

Key words: empirical Bayes, hierarchical Bayes, linear mixed mod-els, poverty indicators, small area estimation

Montoya Imanol (Eustat), Aramendi Jorge (Eustat), Garmendia Ines(Eustat), Iztueta Anjeles (Eustat)

MIXED MODELS FOR LONGITUDINAL DATA WITH APPLI-CATIONS TO SMALL AREA STATISTICS IN THE BASQUE STA-TISTICAL OFFICE

In 2003 the Basque Statistical Office (Eustat) set up a research team to intro-duce small area estimation methods based on models in its statistical produc-

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tion. Small area estimation tries to produce reliable area or domain statisticssuch as means, counts, etc., when the sample data contain too few observations.A common approach is to use statistical models which may “borrow” informa-tion from neighbouring geographical areas or from similar domains in order toachieve such a goal.

Among the results of this project was the application of small area estimationto the Annual Industrial Statistics, published by Eustat in 2005; to the Surveyon the Population in Relation to Activity (2008); to the Survey on InformationSociety – Families (2009); to the Information Technology Survey (2010); andto the Survey on Information Society in Companies (2010, 2011 and 2012).

The aim of the present study is to assess several mixed models for longitu-dinal data which additionally borrow information over time with applicationto data from the Survey on Information Society in companies of the BasqueCountry in 2010, 2011 and 2012 and the Basque Official Registry of Companiesin 2009, 2010 and 2011. This is possible because many of the sampled unitsare observed several times over the years in registers or surveys conducted oncompanies in the Basque Country.

The results from these models show that it is very useful to use data fromprevious surveys or registries for small area estimation. By using previous data,the effective sample size is increased, thus leading to reduced relative estimationerrors, smaller bias and more accurate point estimates. This approach has noadditional cost of data collection as it is based on previous existing data.

Morales Domingo (Miguel Hernandez University Of Elche), PagliarellaMaria Chiara (University of Siena), Salvatore Renato (University ofCassino and Southern Lazio)

PARTITIONED AREA-LEVEL TIME MODELS FOR ESTIMAT-ING POVERTY INDICATORS

The communication deals with small area estimation of poverty indicators.Small area estimators of these quantities are derived from partitioned time-

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dependent area-level linear mixed models. The introduced models are usefulfor modeling the different behavior of the target variable by sex or any otherdichotomic characteristic. The mean squared errors are estimated by explicitformulas. Some simulation experiments are presented. An application to datafrom the Spanish Living Conditions Survey is given.

Munnich Ralf (University of Trier)

SMALL AREA ESTIMATION IN THE GERMAN CENSUS 2011

In 2011, Germany has conducted the first Census after the reunification. Incontrast to a classical census, a register-assisted census using the populationregister data was implemented. An additional sample of approximately 10

The presentation gives an overview of how the sampling scheme was set-upin order fulfil legal requirements and to guarantee an optimal but still flexiblesource of information. Further, I will discuss the results of a research project,financed by the statistical offices of Bund und Lander in Germany, which aimedat investigating the appropriateness of design-based and model-based estima-tion for the German census. Finally, some recent findings and an outlook willbe given.

Munnich Ralf (University of Trier)

SMALL AREA APPLICATIONS: SOME REMARKS FROM ADESIGN-BASED VIEW

The methodological developments of small area estimation have been rapidly

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increasing in the past years. However, small area applications did not grow thatfast. Further, only very few statistical offices already use small area estimatesin production. This is mainly due to higher burden in producing model-basedsmall area estimates. However, National Statistical Offices also have the re-sponsibility to produce figures that can stand legal disputes. As a matter offact, many statistical offices still have to evaluate statistical products as resultsfrom survey data and, hence, have to consider sampling design issues.

The presentation focuses on the impact of sampling designs on small areaestimation methods. Applications are drawn from household and business data.Additionally to sampling designs, methods of benchmarking are considered inorder to provide coherent results between design-based and model-based esti-mates.

Nesa Mossamet Kamrun (University of Wollongong), Clark RobertG. (University of Wollongong), Birrell Carole L. (University of Wol-longong)

ADULTS HEALTH STATUS AND BEHAVIORS IN NEWZEALAND: AN APPLICATION OF MULTIVARIATE FAY-HERRIOTMODEL

In order to improve the overall health condition of a population, accurate esti-mates of health indicators are required at very small spatial scale, typically theadministrative units of a country and/or a region within a country. Standarddirect estimation methods tend to have unacceptably high standard errors, sothat small area estimates(SAEs) based on statistical models allowing ’borrowingof strength’ across areas. Due to the inaccessibility of micro data, the unit levelSAE methods cannot be applied in many situations.Area level SAE methodssuch as Fay-Herriot model (1979) have been widely used instead.

In this research, we consider a multivariate Fay-Herriot model which bor-rowing strength across spatial regions as well as considering multiple variablessimultaneously which are correlated with the variable of interest.The result-

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ing estimates are compared with the corresponding univariate approach.Directarea-level weighted estimates of adult health indicators are obtained from the2011/12 New Zealand Health Survey.A multivariate Fay-Herriot model for pre-dicting small area estimates with their mean squared errors is fitted using ex-planatory variables from the Population Census.

Key words: Health Status and Behaviors, Mean Squared Error (MSE),Multivariate Fay-Herriot Model, Small Area Estimation(SAE).

Okrasa Włodzimierz (Central Statistical Office, Cardinal Stefan Wy-szynski University)

SPATIAL DYNAMICS OF COMMUNITY WELL-BEING. PAT-TERNS OF INEQUALITY OF LOCAL DEPRIVATION, POLAND2004-2012

The paper’s objective is two-fold. One is to present the local community level(proxied by gmina) system of the measurement of well-being (meant as anultimate effect of the regional development processes and policies). This ap-proach basis of data from the Local Data Bank (NUTS5/LAU2) to constructthe multidimensional index of local deprivation (MILD) using factor analysisfor the pre-specified areas of local deprivation, exemplified here for the years2004, 2008, 2010, and 2012 (constituting, by the way, a 3-wave quasi-panel dataset). Due to complementing gminas (elements of the finest division of territorialunits NUTS5/LAU2) with geographical coordinates (centroids of households)a new set of questions becomes possible to be addressed, which constitutes thesecond objective of the paper. Namely, how local deprivation – actually, howreduction in the MILD – has changed over time, given an extraordinary effortsmade for regional development during the past decade (supported mainly bythe UE provided resources)? Has the policy on allocation of these resourcesfollowed the EU’s concern about social and territorial cohesion, and whether –and where (?) – the above resources have contributed to a convergence (beta-

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or sigma- convergence) while lessening inequalities among gminas (both withinpowiats, the next to gmina level of territorial aggregation, NUTS4, and withinvoivodships, NUTS2)? For this, an analysis of the dominant patterns of changesover time involves comparison of actual results of allocation with the simulatedones, based on resource allocation proportional to the MILD, treated here as anindicator of ‘demand for development’, implied also by the spatial justice prin-ciples. For selected voivodships spatial analysis has been conducted in searchfor spatial clusters of changes in well-being. Also, the issue of the dynamics incommunity well-being is being undertaken in a direct way, through the decom-position of inequalities of local (gmina) deprivation (well-being) into within- andbetween- group inequalities (using powiats and voivodships for their grouping),for the specified years - the Theil index was employed to this aim. In general,a big deal of variation in patterns of changes among voivodships - in terms oftheir internal differentiation at the level of gminas - that emerged from the pre-liminary analysis, implies the need to turn attention of the bodies responsiblefor regional development programs to the lowest level of territorial units (togminas). Both as regards allocation of public resources since local community(gmina) is actually the end-unit of the development processes, and from thepublic statistics’ standpoint, due to the need to observe the analyzed changestowards a cohesion from the ‘bottom-up’ perspective.

Okupniak Magdalena (Poznan University of Economics)

MINCER MODEL IN SMALL AREA ESTIMATION

We can observe in economics that there is a big demand for information aboutsalaries on a low level of aggregation. Those data are used to making importantdecisions, for example related to distribution of money, which are intended tosupport and develope a desired region. Using small area estimation methodsallows us to obtain less biased information and specially using auxiliary variablesgain estimates on areas about which we have lack of information that we arelooking for.

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This article is an attempt to combine small area information with Mincermodel, which is used to estimate salaries. The information about years ofeducation and years of work experience were mainly used.

Paradysz Jan (Poznan University of Economics), Paradysz Karolina(Poznan University of Economics)

INDIRECT ESTIMATION OF DISABILITY ON THE BASE OFPOLISH NATIONAL CENSUS 2011

The main objective of the project is a multi-dimensional analysis and forecastingof the development of legal and biological disability in Poland on a regional basisusing statistical data from the National Census of Population and Housing2011(in short NCP 2011) and indirect estimation for small domains and areas.In addition, the project will perform two additional goals of a source-finding andmethodological nature. NCP 2011 was carried out for the first time in Polandwith the use of administrative data sources and survey methodology and smalldomain statistics (small area estimation). Thus, it will be a first evaluation ofthese sources and a wide range of indirect estimation techniques in the study ofpeople with disabilities. As the research hypothesis we will test on this data thedistributions of persons with disabilities in age and space for the analysis andforecast in the context of an ageing population. The end result of our study isto verify the thesis about the existence of effects of tempo in real generationson the development of measures in cross-sectional approach (the demographictranslation problem).

Thanks to the project we can expect significant progress in the developmentof indirect estimation methodology. The focus is particularly on testing thenew estimators that take into account integrated administrative records (goldenrecord) as a source of auxiliary variables, calibration of complex units (families,households) and benchmarking in small area estimation. From the perspectiveof cognitive values, a detailed analysis can be expected of distributions of peoplewith disabilities at the local level (municipalities, cities and counties), for which

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- to the present organization of the NCP 2011 - indirect estimation is the onlysource of information. When assessing the results of the Polish population,disability in terms of the aging population cannot be overlooked. While at thenational and regional Polish level, demographic aging is drawn in rather darkcolours, for many cities and municipalities the age and disability pyramids willbe much worse. The results of our analysis will assist the management of thisdemographic crisis.

Key words: small area statistics, indirect estimation, calibration,benchmarking, disability, census, multistate analysis, demographicprojection, ageing.

Pfeffermann Danny (Southampton University, CBS Israel)

MODEL SELECTION AND CHECKING FOR SMALL AREAESTMATION

I shall divide my talk into two parts. In the first part I shall provide an overviewof some methods proposed in the literature for small area model selection andchecking, distinguishing between Frequentist methods and Bayesian methods.This part of my talk is intended to form the background for the 3 invited talksthat will follow, and which will present new methods. In the second part of mytalk I shall discuss some issues related to the theoretical foundation of smallarea estimation models and in particular, the interpretation and role of therandom effects.

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Potrykowska Anna (The Government Population Council of Poland)

NEW PATTERN OF INTERNATIONAL MIGRATION IN PO-LAND. A MIGRATION POLICY PERSPECTIVE

The phenomenon of migration, which is associated with the globalisation pro-cess of the world’s economy, is also reflected in Poland. The emigration of Polesindicates a tendency of temporary or seasonal departures, which is in accordancewith the migration trends observed in Europe or throughout the world. Thecurrent migration processes and the consequences of implementing the Union’sprinciple of the free flow of workers, are not reflected in the state strategies.Their presence is required in light of the data concerning emigration potentialand emigration resources, as well as the newest CSO demographic projectionsup to the year 2035. The treatment of the migration policy as a permanentelement of the state’s development and modernisation strategy requires it to bebased on solid knowledge regarding the nature and results of migration. Migra-tion statistics are therefore very significant in this field. From the perspective ofthe ensuing migration processes in the world, Poland plays a dual part. On theone hand, we are observing the phenomenon of migration for work and perma-nent residence by Poles, mainly to other highly developed countries. From thisperspective, Poland can be described as a source country. On the other hand, toa considerably lesser extent, we are dealing with an inflow of young persons toPoland seeking employment and/or wishing to settle here, or to continue theireducation. Above that, even refugees are looking for shelter in our country, soimmigrants escaping armed conflict and other disasters which confronted themin their own countries. We are also noting returns of country mates on the basisof the Repatriation Act. Finally completing the picture of immigrant inflow toPoland are persons who attempt to cross the border illegally. Poland thereforealso plays the role of a destination country. The migration process from and toPoland is therefore very complicated and, due to this, difficult to capture, andadditionally its nature is very dynamic, as can be observed with the changesrelated to the worsening of the economic situation in the world. Due to the

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mobility of a seasonal character and the circulation of young persons, it is aproblem to gather reliable data, especially considering that Poles have a rightto travel in the EU without any barriers. This paper contains an analysis ofyouth migration, with a particular emphasis on features specific to 2009-2011,in relation to their dynamics and changes taking place in the previous years.

Pratesi Monica (University of Pisa), Giannotti Fosca (University ofPisa), Giusti Caterina (University of Pisa), Marchetti Stefano (Uni-versity of Pisa), Pedreschi Dino (University of Pisa), Salvati Nicola(University of Pisa)

AREA LEVEL SAE MODELS WITH MEASUREMENT ERRORSIN COVARIATES: AN APPLICATION TO SAMPLE SURVEYSAND BIG DATA SOURCES

The timely, accurate monitoring of social indicators, such as poverty or inequal-ity, at a fine grained spatial and temporal scale is a challenging task for officialstatistics, albeit a crucial tool for understanding social phenomena and policymaking. Big data sensed from the digital breadcrumbs that humans leave be-hind in their daily activities mediated by the ICT’s are in fact providing evermore accurate proxies of social life. In this paper we present an area level smallarea model with covariates obtained by Big Data analysis to predict povertyrates at local level in Tuscany. To use these data as covariate information, wepropose to apply the modified version of the Fay-Herriot model proposed byYbarra and Lohr (2008) to allow for measurement error in covariates. Socialindicators resulting from Big Data are also used as a term of comparison to tra-ditional poverty indicators predicted by small area models in the same region.The results give evidence on potentialities and limits of the usage of big datatogether with small area estimation techniques to show how big data have thepotential to mirror aspects of well-being and other socio-economic phenomena.

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Rao J. N. K. (Carleton University)

INFERENTIAL ISSUES IN MODEL-BASED SMALL AREA ES-TIMATION: SOME NEW DEVELOPMENTS

Rapid developments in theory and applications of small area estimation havetaken place especially over the past 15 years or so in response to growing demandfor reliable small area statistics. In this talk, after highlighting some key pastcontributions, I will appraise some recent important developments under arealevel and unit level models, mainly addressing issues related to assumed models.I will also cover bootstrap methods for mean squared error (MSE) estimationand confidence interval construction. Recent work on robust estimation of smallarea means will be mentioned. Issues related to informative sampling will beaddressed. New developments in model selection and checking will also becovered. Methods for the estimation of complex parameters such as small areapoverty measures will be presented along with an application to Spanish Surveyon Income and Living Conditions. Role of ’big data’ in small area estimationwill be discussed.

Roszka Wojciech (Poznan University of Economics)

CREATING SMALL AREA SPATIAL MICRODATA FOR MUL-TIDIMENSIONAL LABOR MARKET ANALYSIS

Measurement of the labor market is one of the most important tasks of officialstatistics. In Poland, the Labor Force Survey is the largest sample survey interms of coverage. Even though, the sample size allows to create estimates

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of satisfactory quality only in a few cross sections. Census is performed rela-tively rare. In inter-census periods an information gap arises that deprives localgovernments in access to basic characteristics of the socio-economic life.

Dynamically developing trend of spatial microsimulation allows to createmulti-dimensional estimates for small domains. Microsimulation can be definedas a methodology that is concerned with creation of large-scale population mi-crodatasets. It involves merging of census or administrative registers and surveydatasets to analyze and simulate population which characteristics are as closeto the real population as it is possible to estimate.

The aim of the paper is to assess the possibility of obtaining multivariate es-timates of acceptable quality for labor market. By combining information fromcensus and the Labour Force Survey in Poland in 2011, synthetic microdataset will be created in order to estimate the economic activity of the populationin terms of counties (NUTS 4), gender and level of education. The study is asimulation and the quality of the obtained estimates will be assessed, amongothers, by comparison with the results of the census.

Key words: data integration, small area estimation, spatial microsim-ulation

BIBLIOGRAPHY

1. Ballas D., Rossiter D., Thomas B., Clarke G.P., Dorling D. 2005, Geog-raphy Matters: Simulating the Local Impacts of National Social Policies,York, Joseph Rowntree Foundation, UK

2. Rahman A. 2008, A Review of Small Area Estimation Problems and Method-ological Developments, Discussion paper 66, NATSEM, University of Can-berra

3. Rao J. N. K. 2003, Small Area Estimation, Wiley and Sons

4. Tanton R., Edwards K. 2013, Spatial Microsimulation: A Reference Guidefor Users (Understanding Population Trends and Processes), Springer

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Salvatore Renato (University of Cassino and Southern Lazio),Pagliarella Maria Chiara (University of Siena)

SPATIO-TEMPORAL TIME-VARYING EFFECTS MODELS ANDSTATE-SPACE MODELS WITH SPATIAL STRUCTURE: AN AS-SESSMENT OF THEIR EFFICIENCY IN SMALL AREA ESTIMA-TION

One of the targets of the small area estimation models is to derive empiricalbest linear unbiased predictors of totals and means of survey variables, throughtwo categories of models: area level (Fay-Herriot) and unit level models, andtheir generalizations. The main feature of the unit level models is that dataare available at the level of the survey units. When this type of data are notavailable, small area estimation is also reliable, with the adoption of the Fay-Herriot model. Extensions of both type of these classic small area models arediscussed in a wide and analytic literature. In particular, two different typeof extensions of these models have been studied: the first considers the spatialautocorrelation amongst the small areas. With the second, it is possible to sharecommon regression and time autocorrelation parameters in the model, fromtime series data. Spatio-temporal models exploit these two kind of abilities in acommon model, because we consider simultaneously both spatial and temporalautocorrelation. This allows to achieve the maximum gain of efficiency of themodel-based estimates in related small areas.

One of the relevant aspects that arise in model-based small area estimation,is the question of treating the neighborhood similarities of the small areas inthe regression model. Due to these common characteristics, the neighboringareas have a spatial dependence. Simultaneous dependence and conditionaldependence among the random-area effects have been studied, following thestructure of Simultaneusly Autoregressive (SAR) and Conditionally Autore-gressive (CAR) models. The major part of the research works concern the arealevel Fay-Herriot model, with a spatial structure defined by the simultaneousspatial dependence. There are two fundamental different ways of modeling the

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lattice information (i.e. the areas) in both SAR and CAR models. A spatialweight matrix containing row standardized distances between the centroids ofthe areas, or based on the length of the common boundary of them, is incor-porated in the model. In alternative, a row standardized proximity matrix isconsidered, derived from a boolean neighborhood index that specify whetherthe areas are neighbors or not. Spatial small area estimation models are definedby a three-stage (area level) or a two-stage (unit level) linking model, includingin one of these stages the modeling the spatial dependence through a regressionmodel on the vector of the random-area effects.

The so-called spatio-temporal models extend spatial models to time seriesdata. These models strengthen survey direct estimators by considering space-time correlated random-area effects. There are many case studies that demon-strated that Eblup estimators receive a significant gain in efficiency when theestimated between-time variation is relatively small. This property is partic-ularly relevant. For example, in area level models, in the situation in whichthe estimated area sampling variances are very heterogeneous respect to therandom-area time variation. Spatial models can include temporal correlation indifferent ways. The most common models include time-varying effects in within-area by time observations (areas or units). The classic Fay-Herriot model canbe extended after including a space-time effect to the usual random-area effectin the regression equation. In most studies, the time-varying effects usually fol-low an AR(1) process. Otherwise, random walk process, seasonal effects, andmore complicated temporal models with random slopes have been studied. Inorder to consider time correlation in small area estimation, a further approach isbased on the state-space models. These models performs estimates at area andtime updating the model estimates over time by the Kalman filter equations.At the time instant t, the Eblup estimators of the state vector (in the modeltransition equation), that define the so—called mesurement equation - i.e., thevector of the model fixed and random effects - are obtained on the basis of thedata observed up to time (t − 1).

We assess the efficiency of the small area models (area level and unit levelspatio-temporal models) under simulataneous spatial dependence, comparingthe efficiency of the linear models with time-varying effects respect to the statespace models with spatial structure. We also discuss the second-order approxi-mation of the mean squared error of the empirical predictor under these differentstrategies. A simulation study is conducted, based on the time series of Eu-Silcdata.

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Singh Trijya (Le Moyne College)

ESTIMATION OF RATES AND PROPORTIONS FOR SMALLAREAS WHEN COVARIATES ARE MEASURED WITH ERROR

In many studies where the response variable is binary, the primary interestis to estimate the proportion of individuals possessing certain characteristicsin the population and also in its small areas. In small area estimation, suchstudies are quite common for disease or poverty mapping and also for estimatingunemployment rates in the counties or districts of a country. Several authorshave proposed procedures for estimating small area rates and proportions usingEBLUP, EB and HB predictors. In all these approaches, the models utilizeauxiliary information to borrow strength from other small areas. Quite often,some, if not all, covariates are measured with error which induces bias in theestimates of xed e ects parameters and hence in the predictor. The meansquared error of the predictor in such situations is enhanced and the predictormay perform worse than even the direct estimator in some situations. In thepresent paper, we have proposed methods to correct the bias in the estimatesincurred due to the measurement error to increase the efficiency of EBLUP.Through simulations, we have demonstrated the gain in e ciency due to theproposed corrections. We have applied these methods to a model consideredby Gonzales-Manteiga et al. (2007) and also for the area level model given byJ.N.K. Rao (2003).

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Steorts Rebecca C. (Carnegie Mellon University)

CONSTRAINED SMOOTHED BAYESIAN ESTIMATION

We develop methods of constrained Bayes estimation for small-area estimation.We deal with two kinds of constraints: those that require smoothness with re-spect to some form of similarity across areas, such as geographic proximity ora clustering by covariates; and bench-marking constraints, requiring (weighted)means and variances of estimates to agree across levels of aggregation, or withexternal sources of information. We develop our tools for constrained estimationboth geometrically, by projecting the unconstrained Bayes estimate into the fea-sible set, and decision-theoretically, by minimizing the posterior risk. We showthat our constrained estimators can be obtained as solutions to tractable con-vex optimization problems, and in some cases get closed-form solutions. Mean-squared errors of the constrained estimators are calculated via bootstrapping.Our techniques are free of distributional assumptions, and equally applicablewhether the estimator is linear or non-linear, univariate or multivariate. Weillustrated our methodology by applying it to data from the U.S. Census’s SmallArea Income and Poverty Estimates program.

Sugasawa Shonosuke (University of Tokyo), Kubokawa Tatsuya (Uni-versity of Tokyo)

ESTIMATION AND PREDICTION INTERVALS IN TRANSFOR-MED LINEAR MIXED MODELS

For analyzing positive or bounded data, we suggests parametrically transformed

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nested error regression models (TNERM), which not only include the log-transformed model, but also adjust exibly the transformation parameter tot the data to a normal linear regression. Conditions on the transformation arederived for consistency of the maximum likelihood estimator for the transforma-tion parameter. The conditions are satisfied by the dual power transformationfor positive data and the dual power logistic transformation (we newly propose)for bounded data. In order to calibrate uncertainty of the transformed empiricalbest linear unbiased predictor (TEBLUP), we derives prediction intervals withsecond-order accuracy based on the parametric bootstrap method. Conditionalprediction intervals given data in the area of interest are also constructed. Theproposed methods are investigated through simulation and empirical studies.This talk is based on the following preprint paper.

BIBLIOGRAPHY

1. S. Sugasawa and T. Kubokawa (2014). Estimation and prediction intervalsin transformed linear mixed models. Discussion Paper Series, CIRJE-F-929.

Sumonkanti Das (University of Wollongong)

BACK TRANSFORMATION BIAS IN POVERTY MAPPING

The most widely used small area estimation method for poverty mapping isthe World Bank method, also known as the ELL method (Elbers, Lanjouw andLanjouw, 2001). Under this approach, the survey values of a skewed responsevariable (typically household income or expenditure) are transformed to logscale and regressed on explanatory variables in order to develop a two-levellinear regression model with households at level one and clusters (often villagesor collections of villages) at level two. The estimated regression coefficientsand the estimated cluster-level and household-level components of variance ob-tained from this regression model are then used to generate log-scale predictedvalues for the survey population via a bootstrap procedure. Finally, these pre-

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dicted values are back-transformed to their original scale and compared with apoverty line in order to estimate the small area values of poverty indicators. Indoing so, it is assumed that the bootstrap procedure eliminates the bias of theback transformation (Jones and Hasselt, 2004). Chandra and Chambers (2011)show analytically that predicted values for a skewed response obtained via backtransformation can still have significant bias. However, there does not appearto have been any analytical or numerical work on potential back transformationbias in ELL methodology. In this paper I use both numeric as well as analyticmethods to investigate whether the back transformed data generated in theELL procedure remains unbiased when the ELL assumptions are satisfied andalso when they are violated. A diagnostic procedure to check the unbiased ofthe back-transformed data is also investigated.

Key words: ELL Methodology; Skewed Response Variable; SmallArea Estimation; Mixed Models.

Szymkowiak Marcin (Poznan University of Economics, Statistical Of-fice in Poznan), Wawrowski Łukasz (Poznan University of Economics,Statistical Office in Poznan), Młodak Andrzej (Statistical Office inPoznan)

MAPPING POVERTY AT THE LEVEL OF SUBREGIONS INPOLAND USING INDIRECT ESTIMATION

The European Survey on Income and Living Conditions (EU-SILC) is the basicsource of information published by GUS (Central Statistical Office in Poland)about the relative poverty indicator both for the country as a whole and at theregional level (NUTS 1). Estimates at lower levels of territorial division thanregions (NUTS 1) or provinces (NUTS 2, in Poland called ”voivodships”) havenot been published so far. These estimates can be calculated by means of in-direct estimation methods which rely on information from outside the domainof interest, which usually increases estimation precision. Since the estimation

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process used in these techniques is model-based (i.e. the EBLUP estimatorbased on the Fay–Herriot model), the indirect estimation methodology posesa challenge for official statistics in many countries. The main aim of this pre-sentation is to show chosen results of estimation of the poverty indicator at alower level of spatial aggregation than the one used so far, i.e. at the level ofsubregions in Poland (NUTS 3). Territorial analyses of the scope of poverty inPoland at the level of NUTS 3 will be also discussed and presented in details.

Key words: EU-SILC, poverty, direct estimation, indirect estimation,EBLUP estimator, Fay–Herriot model.

BIBLIOGRAPHY

1. Bedi, T., Coudouel, A., Simler, K. (2007), More Than A Pretty Pic-ture. Using Poverty Maps to Design Better Policies and Interventions,The World Bank, Washington D.C., USA. Center for Small Area Estima-tion (2013), Poverty Maps at the Subregional Level in Poland Based onIndirect Estimation, Poznań, Poland (unpublished manuscript).

2. GUS (2012), Incomes and living conditions of the population in Poland(report from the EU-SILC survey in 2011), Central Statistical Office ofPoland, Social Surveys and Living Conditions Department, Statistical Pub-lishing Establishment, Warsaw, Poland.

3. GUS (2013), Life quality. Social capital, poverty and social exclusion inPoland (in Polish), Central Statistical Office of Poland, Social Surveysand Living Conditions Department, Statistical Publishing Establishment,Warsaw, Poland.

4. Rao J.N.K. (2003), Small Area Estimation, John Wiley & Sons, Inc., Hobo-ken, New Jersey.

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Tran Bac (U.S. Census Bureau), Lahiri Partha (University of Mary-land)

AN EVALUATION OF DESIGN-BASED PROPERTIES OF DIF-FERENT SMALL AREA ESTIMATORS USING DATA FROM THEU.S. ANNUAL SURVEY OF PUBLIC EMPLOYMENT AND PAY-ROLL

There are a large number of small area estimators available in the literature.These estimators typically use either implicit or explicit models to combine sur-vey data with different administrative and census records. The properties ofsuch an estimators are usually studied using the model used to derive the esti-mator. However, the design-based properties of small area estimators, which aremost appealing to the survey practitioners, are largely unknown. The AnnualSurvey of Public Employment and Payroll (ASPEP), conducted by the Govern-ment Division of the U.S. Census Bureau, provides statistics on the number offederal, state, and local government civilian employees and their gross payrolls.Different small area estimators can be produced using the ASPEP data andauxiliary information from the preceding Census of Governments. We developa design-based Monte Carlo simulation experiment in which we draw repeatedsamples from the 2007 Census of Governments data using the ASPEP samplingdesign and compute a wide range of estimates that use the generated sampleand the 2002 Census of Government data. We then compare simulated design-based biases, variances, mean squared errors and coverage probabilities of theseestimators. We repeat the experiment using the 2012 Census of Governmentdata in order to understand if these properties change over years. The estima-tors covered under our simulation study includes: Horvitz-Thompson, SPREE,traditional composite, and empirical Bayes and hierarchical Bayes methods.

Key words: Government Units, Monte Carlo Simulation, Compos-ite Estimator, Horvitz-Thompson, Empirical and Hierarchical Bayes,SPREE

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Ugarte Marıa Dolores (Public University Of Navarre), Adın Aritz(Public University Of Navarre), Goicoa Tomas (Public University OfNavarre), Militino Ana Fernandez (Public University Of Navarre),López-Abente Gonzalo (Public University Of Navarre)

SPACE-TIME ANALYSIS OF YOUNG PEOPLE BRAIN CAN-CER MORTALITY IN SPANISH PROVINCES

In this talk the temporal evolution of the geographical pattern of brain cancermortality for people under the age of 20 years will be analyzed in Spanishprovinces during the period 1986-2010. Mortality risk estimation will be carriedout using a battery of spatio-temporal models (Ugarte et al., 2014) extendingthe spatial model originally proposed by Besag et al. (1991). Additional modelsconsidering a two-level structure of spatial effects (Shroedle et al., 2011) will bealso explored because the Spanish provinces are aggregated in larger regions,called Autonomous Regions, who are in charge of their own health systems.The models will be fitted under a fully Bayesian approach and some criteria formodel choice will be used to select the best model. In the analyzed period, 1531deaths have been reported in young people in Spain. Although the prognosisof this cancer has improved since 1995 as it will be shown by the models, theprovincial pattern of mortality has remained fairly stable pointing to the Regionof Navarra as the area of highest mortality.

BIBLIOGRAPHY

1. Besag, J., York, J., and Mollie, A. (1991). Bayesian image restoration, withtwo applications in spatial statistics. Annals of the Institute of StatisticalMathematics, 43(1):1-20.

2. Schroedle, B., Held, L., Riebler, A., and Danuser, J. (2011). Using in-tegrated nested laplace approximations for the evaluation of veterinarysurveillance data from switzerland: a case study. Journal of the RoyalStatistical Society: Series C (Applied Statistics), 60(2):261-279.

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3. Ugarte, M. D., Adin, A., Goicoa, T., and Militino, A. F. (2014). On fit-ting spatio-temporal disease mapping models using approximate bayesianinference. Statistical Methods in Medical Research, Published on line the7th April 2014.DOI:10.1177/0962280214527528

van den Brakel Jan (Maastricht University School of Business andEconomics, Statistics Netherlands), Krieg Sabine (Statistics Nether-lands)

SMALL AREA ESTIMATION WITH STATE-SPACE COMMONFACTOR MODELS FOR ROTATING PANELS

Macro-economic indicators about the labour force, published by national sta-tistical institutes, are predominantly based on rotating panels. Sample sizesof most Labour Force Surveys (LFS) are generally too small to publish suchindicators on a monthly frequency with design-based or model-assisted modesof inference. Pfeffermann (1991) proposed a multivariate structural time seriesmodel for rotating panels. This model is applied to the series of the differentpanel-waves observed within each domain separately and takes advantage ofsample information observed in preceding periods to improve the precision ofmonthly indicators. The model also accounts for rotation group bias and theserial correlation induced by the rotating panel design.

In this paper, the model is extended in two directions. First, the series of alldomains are combined in one multivariate state-space model. It is investigatedto which extend the precision of a monthly indicator about the labour force canbe further improved by modelling the correlation between the trends, seasonalsand the rotation group bias components of the different domains. Particularly,the trend and seasonal components are cointegrated, resulting in more parsi-monious common factor models. Second, different extensions of the model toinclude strongly related auxiliary series are discussed. It is illustrated to whichextend this results in a further improvement of the precision of the monthlylabour force indicators. In addition it is simulated how robust these models are

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for evolutions in the auxiliary series that are not related to real developmentsof the labour force indicators.

The models are applied to real life data obtained with the Dutch LFS. Arelatively parsimonious version of these state-space models is currently usedby Statistics Netherlands to produce official monthly figures about the labourforce.

Key words: Survey sampling, Structural time series modelling, coin-tegration, labour force survey, Kalman filter

van der Weide Roy (The World Bank)

ESTIMATION OF NORMAL MIXTURES IN A NESTED ER-ROR MODEL WITH AN APPLICATION TO SMALL AREA ES-TIMATION OF POVERTY AND INEQUALITY

This paper proposes a method for estimating distribution functions that areassociated with the nested errors in linear mixed models. The estimator incor-porates Empirical Bayes prediction while making minimal assumptions aboutthe shape of the error distributions. The application presented in this paper isthe small area estimation of poverty and inequality, although this denotes byno means the only application. Monte-Carlo simulations show that estimatesof poverty and inequality can be severely biased when the non-normality of theerrors is ignored. The bias can be as high as 2 to 3 percent on a poverty rate of20 to 30 percent. Most of this bias is resolved when using the proposed estima-tor. The approach is applicable to both survey-to-census and survey-to-surveyprediction.

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Warnholz Sebastian (Free University Of Berlin), Schmid Timo (FreeUniversity Of Berlin), Tzavidis Nikos (University of Southampton)

ROBUST FAY HERRIOT ESTIMATORS IN SMALL AREA ES-TIMATION

Applications in the context of Small Area Estimation are sometimes restrictedby the availability of information. Due to reasons of confidentiality only aggre-gated data may be accessible, i.e. direct estimators of area means and variances.In such cases the analyst can still be confronted with outlier contaminated data,which may influence the analysis in an unintended manner. Robust techniquesfor area-level models can be beneficial; however, they only get little attentionin Small Area Estimation. The reasoning is that on the aggregated level of in-formation, outlying observations do not influence the results as strong as theywould on the individual level.

In this presentation we discuss this statement by motivating area-level con-tamination from its source, the unit-level. On unit-level we may consider out-lying areas and outlying observations which result in different compositions onarea-level. To justify our interest in this topic, we will show how the Fay-Herriotestimator will perform in different model-based simulation scenarios as well asa design-based set-up. Also we will show how the Fay-Herriot estimator itselfmay be considered as a robust technique for unit-level data and what we canconclude for practical applications.

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Wawrowski Łukasz (Poznan University of Economics, Statistical Of-fice in Poznan)

ESTIMATION OF POVERTY HEADCOUNT RATIO AT LAU 1LEVEL IN POLAND USING FAY-HERRIOT MODEL

Economic poverty is a problem in every society. Research on living conditionsconducted by official statistics allows the measurement of this phenomenon.These studies include carried out annually Household Budget Survey and theEuropean Survey on Income and Living Conditions. Because of the methodol-ogy, results of the above studies are published on a very general level of spatialaggregation — the country and the region. Information for a more detailedsections are not available due to the small sample size, which leads to largemean square errors of obtained estimates and hence low reliability of theseratings. In order to obtain estimates at lower level of aggregation than it ispublished, small area estimation methods are used. They allow the estimationof parameters beside the small sample size using for this purpose available datasources.

This article attempts to estimate the headcount ratio at LAU 1 level inPoland. This estimation will be possible through the use of data from differentsources describing living conditions of households and the use of Fay-Herriot[1979] model. As a result estimates for previously unpublished level of aggre-gation will be obtained.

Key words: poverty, small sample size, small area estimation, Fay-Herriot model.

BIBLIOGRAPHY

1. Fay, R.E., Herriot, R.A., 1979, Estimates of income for small places: Anapplication of James-Stein procedures to census data, Journal of the Amer-ican Statistical Association 74.

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Weidenhammer Beate (Free University Of Berlin), Tzavidis Nikos(University of Southampton), Schmid Timo (Free University Of Berlin),Salvati Nicola (University of Pisa)

DOMAIN PREDICTION FOR COUNTS USING MICROSIMU-LATION VIA QUANTILES

Domain prediction for count outcomes has typically relied on generalised lin-ear mixed models that make explicit distributional assumptions for example,assuming a Poisson distribution allows for a particular mean-variance relation-ship. In this work we propose a model-based methodology for domain predictionwith count outcomes that potentially allows for more flexibility.

To start with, we use jittering to transform the discrete outcome into a con-tinuous one. A very fine grid of quantiles of the continuous outcome is beingestimated by using a linear quantile model with domain random effects. Util-ising the one to one relationship between the quantiles of the jittered outcomeand those of the discrete outcome, allows us to effectively estimate a fine gridof quantiles for the discrete outcome. An estimator of the domain-specific pop-ulation distribution function, and hence of the target parameters, is obtainedvia microsimulation using Monte Carlo samples.

Using model-based simulation studies, the proposed methodology is com-pared to alternative domain predictors. Mean Squared Error estimation viaparametric bootstrap is discussed and some first results are presented.

Key words: Small area estimation, Linear quantile random effects,Jittering, Monte Carlo simulation

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Wieczorek Jerzy (Carnegie Mellon University), Pane Michael (Car-negie Mellon University), Steorts Rebecca C. (Carnegie Mellon Uni-versity)

STRUGGLES IN SMALL AREA ESTIMATION: BENCHMARK-ING AND WEIGHTING

The study of small area estimation is developing lately, due to growing demandfor more precise estimation in small domains and to increasing awareness ofthese methods’ utility. Recent applications range from Google trends to en-vironmental applications and even sport statistics. Furthermore, within smallarea estimation, benchmarking is useful tool when noisy subgroups must beforced to aggregate. We review one- and two-stage benchmarking procedures,reframing them as simple convex optimization problems. We illustrate bothmethods through two real applications for the benefit of practitioners, showinghow to choose loss function weights and explaining open issues that can beproblematic for users.

Wilak Kamil (Poznan University of Economics)

TREND ESTIMATION IN LABOUR FORCE SURVEY IN PO-LAND

Labour Force Survey (LFS) in Poland is a quarterly panel survey with rotating-panel sampling schemes. The sample for each quarter consists of four elemen-tary samples. In a given quarter there are two elementary samples surveyedin the previous quarter, one elementary sample introduced into the survey for

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the first time and one elementary sample which was introduced into the surveyexactly a year before. In connection with the above scheme, population param-eters in subsequent quarters are estimated partially on the basis of the sameunits, hence survey errors can be correlated.

Estimation of autocorrelation coefficients of survey errors by classical estima-tors is not possible because the survey errors are not observable. This problemwas considered by Pfefferman et al. (1997). They derived the estimator, whichwas used to estimate of autocorrelation coefficients of survey errors of unem-ployment rate and participation rates in Labour Force Survey in Australia andUSA.

Knowledge about autocorrelation of survey errors in LFS is important fortrend estimation of labour market parameters. Omission of autocorrelation insurvey errors may cause that the trend curve will be biased by fluctuations,which are characteristic for autoregressive processes. These fluctuations maybe significant in estimating for small domains, where survey errors are relativelylarge. Pfefferman et al. (1997) proposed to use dynamic linear models for trendestimation. In their method, autocorrelation of survey errors is included byinsertion of autoregressive components to model. Results of their study showedthat estimates of trends, which comprise autocorrelation of survey errors, aremore smooth and stable.

This article is an attempt to estimate trend of unemployment rate in Wielko-polska Voivodeship (NTS2) for six domains (by sex and three age groups: 15-24,25-44, 45-59/64). For this purpose Pfefferman’s method was adjusted to LFSin Poland. Special attention was put to quality assessment of the estimates,including bias and precision. Estimation was conducted for data from the years2000-2010.

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Williamson Paul (University of Liverpool), Morrissey Karyn (Uni-versity of Liverpool), Espuny-Pujol Ferran (University of Liverpool)

SURVEY REWEIGHTING AS A MEANS TO SAE

Survey reweighting provides an alternative approach to small-area estimation.In conventional survey reweighting, survey responses are weighted ‘upwards’ tomatch known national or regional control totals. For the purposes of small-areaestimation local rather than national control totals are used, with the resultthat survey responses are weighted ‘down’ rather than ‘up’. The key challengesto this approach are: (i) selecting control totals relevant to the small-area char-acteristic being estimated; (ii) finding a set of weights that ‘optimally’ satisfythese control totals. This paper reviews the main strengths and weaknessesof survey reweighting as an SAE technique; surveys the range of algorithmsadopted to produce ‘optimal’ weights; evaluates their relative performances;and provides some applied examples of reweighing-based SAEs ‘in action’.

Wywiał Janusz (Katowice University of Economics)

ON SAMPLING DESIGN PROPORTIONAL TO FUNCTIONOF AUXILIARY VARIABLE ORDER STATISTICS

An estimation of the population parameters in finite and fixed populationsis taken into account. The estimation is focused on a population’s averageof a variable under study. Values of the variable under study are observedin random samples selected according to pre-assigned sampling designs andsampling schemes. We assume that values of an auxiliary variable is observed

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in the whole population. The population average is estimated by means of asampling strategy which is the pair of an estimator and a sampling design.

The well known Horvitz-Thompson statistic, as well as ratio or regressiontype estimators, are taken into account. The consideration are focused on thenew class of sampling designs proportional to functions of order statistics of theauxiliary variable. The sampling design proportional to the value of the positiveorder statistic as well as proportional to the sum or difference of the two orderstatistics are studied. Moreover, their conditional versions are defined, too.

The main purpose of this paper is a presentation of the basic properties ofsampling strategies based on the sampling designs dependent on quantils. Forinstance some their conditional versions are characterized by inclusion probabil-ities almost proportional to the values of the auxiliary variable. Moreover, thereare possibilities of application the conditional version of the sampling design ininference about domain means as it was shown in the previous section.

Especially, their accuracy will be analyzed. Comparison of accuracy of thedefined sampling strategies is based on the computer simulation analysis. Ingeneral, the conclusions let us choose the sampling strategy for particular sit-uations determined by population distributions of a variable under study andauxiliary variables

Yavuz Turac (Turkish Statistical Institute), Kocak, N. Alpay (Turk-ish Statistical Institute), Uslu Enes E. (Turkish Statistical Institute)

SMALL AREA ESTIMATION WITH DATA MINING TECH-NIQUES: A CASE STUDY FOR TURKEY

In terms of the indirect estimation perspective in small area estimation, usingmixed approach (with fixed or random effect) is preferable in the case of capa-bilities of sample-design (variance properties of small domains) are convenient.If not, small area estimation becomes model-based process, entirely. Then,one has to focus to find at first efficient model estimation techniques and thenproper covariates. In this paper, it is examined such a case which the aim is

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to estimate the gross value added for provinces of Turkey, where the mixed ap-proach is not convenient and mostly focused advanced modeling techniques, i.e.data mining techniques with a lot of covariates. As a consequence, the modelperformance is found successful when it is evaluated with proper diagnostics.This finding is encouraged us about that the data mining techniques can beimplement throughout the small area estimation process.

Zhang Junni (Peking University), Bryant John (Statistics New Zealand)

FULL BAYESIAN BENCHMARKING OF SMALL AREA ESTI-MATION MODELS

Many practical applications require sensible estimates of means, rates, or to-tals for small areas within a larger population. Benchmarking is a standardtechnique within the field of small area estimation. Benchmarking a small areaestimation model entails constraining the disaggregated model-based estimatesto agree with the benchmarks. In this paper, we develop and apply a fullBayesian interpretation of benchmarking for general small area models. Ourapproach allows for vector benchmarks, and does not require that all unitsare benchmarked. The benchmarks may be treated as certain or uncertain,and may be internal and external. In all cases, complete posterior distribu-tions are generated, rather than point estimates. We examine the propertiesof benchmarked models using an analytical example and several simulations.The evaluation focuses on internal benchmarking because its benefits are lessobvious given that it uses the same data for the benchmarks and modelling.

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Zhang Li-Chun (University of Southampton), Whitworth Alison (Of-fice for National Statistics)

BENCHMARKED SYNTHETIC SMALL AREA ESTIMATION

Benchmarking of SAEs to accepted estimates or known totals achieves output(arithmetic) consistency that is of essential importance in Official Statistics.Benchmarking of aggregated mixed-effects-model-based SAEs has received at-tention in the literature (e.g. Pfeffermann, 2013, Section 6.3). But there is alimit to which sample survey data can support the various mixed-effects mod-els. As one descends the hierarchy of aggregation, sooner or later, a level willbe reached where many (or most) areas are not represented in the sample, andmany areas will have only very few sample observations. Synthetic estimates arenecessary from there on. At the same time, the stringent assumptions that arenecessary for the synthetic estimates to be unbiased are often plainly unattain-able. Benchmarking of synthetic estimates acquire thus an urgent aspect ofactively reducing the model (or definitional) bias.

In this work we explore two approaches to benchmarked synthetic SAEs. Inthe first case, the solution is presented as optimal adjustment of the initial un-constrained synthetic estimates subjected to the benchmark constraints. Thisresembles the benchmarked BLUP approach (Wang et al., 2008) for mixedeffects models. We show that many common one-way and multi-way bench-marking practices can be included as special cases. The second approach maybe referred to as structural modeling, where the benchmark constraints are in-tegrated into the model for deriving synthetic estimates. An example is thegeneralized SPREE (GSPREE) models (Zhang and Chambers, 2004; Luna-Hernandez and Zhang, 2014). Empirical data will be used for illustration.

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Zhang Li-Chun (University of Southampton)

CENSUS AND SAE: POPULATION SIZE ESTIMATION

Among the many connections between census and small area estimation (SAE),I focus on the topic of census or census-like population size estimation in thistalk. I review the most common traditional direct estimation methods, aswell as some new developments in the treatment and modelling of enumerationcoverage errors, and discuss some perceived challenges to the indirect estimationof local population sizes.

Żądło Tomasz (University of Economics in Katowice)

ON MEASURING PREDICTION ACCURACY IN SMALL AREAESTIMATION IN THE MULTIVARIATE CASE

The accuracy in the case of estimation or prediction of many parameters isusually assessed based on the trace of variance-covariance matrix, the determi-nant of the variance-covariance matrix or the spectral radius of the variance-covariance matrix. But the statistics are not useful for practitioners due tothe complexity and the problem of their interpretations. We study the modelapproach in small area estimation and joint prediction accuracy of EBLUPsof domain totals. We propose two statistics: quantiles of joint distribution ofabsolute prediction errors and quantiles of joint distribution of absolute relativeprediction errors. Estimators of the statistics are proposed as well. The con-siderations are supported by Monte Carlo simulation study based on the realdata.

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Contact

Chairperson of the Organizing Committee

dr Marcin Szymkowiakemail: [email protected]

Office of the Organizing Committee

Department of StatisticsFaculty of Informatics and Electronic Economyal. Niepodleglosci 1061-875 PoznanPolandPhone: +48 61 854 39 35e-mail: [email protected]: www.sae2014.ue.poznan.pl

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3-5September,Poznan,Poland

List of Participants

Surname and Name E-mail Institution

Adach-Stankiewicz Ewa [email protected] Central Statistical Office

Al-Awadhi Mohammad [email protected] College of Business Studies

Aramendi Rique Jorge [email protected] Eustat

Arima Serena [email protected] Sapienza University of Rome

Articus Charlotte [email protected] University of Trier

Baldermann Claudia [email protected] Free University of Berlin

Bell William [email protected] U.S. Census Bureau

Beręsewicz Maciej [email protected] Poznan University of Economics, Statistical Office in Poznan

Berg Andreas [email protected] Federal Statistical Office of Germany

Bikauskaite Agne [email protected] Sogeti Luxembourg S.A.

Błachut Barbara [email protected] Statistical Office in Rzeszow

Boonstra Harm Jan [email protected] Statistics Netherlands

Boubeta Martınez Miguel [email protected] University of A Coru Na

Breidt Jay [email protected] Colorado State University

Bryza Magdalena [email protected] Statistical Office in Poznan

Buelens Bart [email protected] Statistics Netherlands

Buhłak Katarzyna [email protected] Statistical Office in Poznan

Burgard Jan Pablo [email protected] University of Trier

Chambers Ray [email protected] University of Wollongong

Ciginas Andrius [email protected] Vilnius University

Das Sumonkanti [email protected] University of Wollongong

Datta Gauri S. [email protected] U.S. Census Bureau, University of Georgia

Dehnel Grazyna [email protected] Poznan University of Economics, Statistical Office in Poznan

Deva Mirela [email protected] Institute of Statistics of Albania

Dygaszewicz Janusz [email protected] Central Statistical Office

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SmallAreaEstimation

2014Dziaduch Sławomir [email protected] Statistical Office in Lublin

El-Horbaty Yahia [email protected] University of Southampton

Erciulescu Andreea [email protected] Iowa State University

Esteban Maria Dolores [email protected] Miguel Hernandez University of Elche

Fabrizi Enrico [email protected] University of Naples Federico Ii

Fasulo Andrea [email protected] The National Institute for Statistics

Franco Carolina [email protected] U.S. Census Bureau

Fuller Wayne [email protected] Iowa State University

Gershunskaya Julie [email protected] U.S. Bureau of Labor Statistics

Ghosh Malay [email protected] University of Florida

Golata Elzbieta [email protected] Poznan University of Economics

Grech Marice [email protected] National Statistics Office in Malta

Gruchociak Hanna [email protected] Poznan University of Economics

Grygiel Grzegorz [email protected] Statistical Office in Poznan

Gubernat Anna [email protected] Statistical Office in Krakow

Haslett Stephen [email protected] Massey University

Hetmańska Aurelia [email protected] Statistical Office in Katowice

Hidiroglou Michel [email protected] Statistics Canada

Hobza Tomas [email protected] Czech Technical University in Prague

Jakóbik Krzysztof [email protected] Statistical Office in Krakow

Jastrzębski Piotr [email protected] Statistical Office in Poznan

Józefowski Tomasz [email protected] Statistical Office in Poznan

Kalton Graham [email protected] Westat

Kamińska Magdalena [email protected] Statistical Office in Szczecin

Kamińska-Gawryluk Ewa [email protected] Statistical Office in Bialystok

Karlberg Forough [email protected] Luxembourg Statistical Services

Kawakubo Yuki [email protected] University of Tokyo

Keto Mauno [email protected] Mikkeli University of Applied Sciences

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Kępa Ewa [email protected] Statistical Office in Bialystok

Klimanek Tomasz [email protected] Poznan University of Economics, Statistical Office in Poznan

Klimek Magdalena [email protected] Statistical Office in Wroclaw

Kołeczek Bogusława [email protected] Statistical Office in Zielona Gora

Kordos Jan [email protected] Central Statistical Office

Kowalewski Jacek [email protected] Statistical Office in Poznan

Krapavickaite Danute [email protected] Vilnius Gediminas Technical University

Kubacki Jan [email protected] Statistical Office in Lodz

Kunicka Elwira [email protected] Statistical Office in Opole

Lahiri Parthasarathi [email protected] University of Maryland

Lee Jarod (Yan Liang) [email protected] University of Technology Sydney

Lehtonen Risto [email protected] University of Helsinki

Litwinski Michal [email protected] Statistical Office in Poznan

Lombardıa Marıa J. [email protected] University of A Coru Na

López Vizcaıno Esther [email protected] Galician Institute of Statistics

Małasiewicz Anna [email protected] Statistical Office in Poznan

Markocka Małgorzata [email protected] Statistical Office in Rzeszow

Markowski Krzysztof [email protected] Statistical Office in Lublin

Martens Beata [email protected] Statistical Office in Zielona Gora

Młodak Andrzej [email protected] Statistical Office in Poznan

Mokhtarian Payam [email protected] University of Wollongong

Molina Isabel [email protected] Charles III University of Madrid

Montoya Imanol [email protected] Eustat

Morales Domingo [email protected] Miguel Hernandez University of Elche

Morze Marek [email protected] Statistical Office in Olsztyn

Mroczkowski Marek [email protected] Central Statistical Ofiice

Munnich Ralf [email protected] University of Trier

Najman Krzysztof [email protected] Statistical Office in Gdansk

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SmallAreaEstimation

2014Nesa Mossamet [email protected] University of Wollongong

Okrasa Włodzimierz [email protected];[email protected] Central Statistical Office, Cardinal Stefan Wyszynski University

Okupniak Magdalena [email protected] Poznan University of Economics

Owczarkowski Artur [email protected] Statistical Office in Poznan

Pahkinen Erkki [email protected] University of Jyvaskyla

Paradysz Jan [email protected] Poznan University of Economics, Statistical Office in Poznan

Paradysz Karolina [email protected] Poznan University of Economics

Parysek Wiktor [email protected] Statistical Office in Bydgoszcz

Petrucci Alessandra [email protected] University of Firenze

Pfeffermann Danny [email protected] University of Southampton, Cbs Israel

Piasecki Tomasz [email protected] Statistical Office in Lodz

Pietrzak Michał [email protected] Statistical Office in Poznan

Potrykowska Alina [email protected] Central Statistical Office

Potyra Maciej [email protected] Central Statistical Office

Pratesi Monica [email protected] University of Pisa

Ramosacaj Miftar [email protected] University of Vlora Albania

Rao J. N. K. [email protected] Carleton University

Rendtel Ulrich [email protected] Free University of Berlin

Ręklewski Marek [email protected] Statistical Office in Bydgoszcz

Rogalewska Anna [email protected] Statistical Office in Bialystok

Rogalinska Dominika [email protected] Central Statistical Office

Roszka Wojciech [email protected] Poznan University of Economics

Rozkrut Dominik [email protected] Statistical Office in Szczecin

Rudys Tomas [email protected] Vilnius University

Ryan Louise [email protected] University of Technology Sydney

Rynarzewska-Pietrzak Beata [email protected] Statistical Office in Poznan

Salvatore Renato [email protected] University of Cassino And Southern Lazio

Schmid Timo [email protected] Free University of Berlin

100


Sexton Christine [email protected] Office for National Statistics

Singh Trijya [email protected] Le Moyne College

Sobków Alicja [email protected] Statistical Office in Wroclaw

Steorts Beka [email protected] Carnegie Mellon University

Stopiński Paweł [email protected] Statistical Office in Bydgoszcz

Sugasawa Shonosuke [email protected] University of Tokyo

Suntornchost Jiraphan [email protected] Chulalongkorn University

Szkop Alina [email protected] Statistical Office in Poznan

Szymkowiak Marcin [email protected] Poznan University of Economics, Statistical Office in Poznan

Śliwicki Dominik [email protected] Statistical Office in Bydgoszcz

Tran Bac [email protected];[email protected] U.S. Census Bureau

Tzavidis Nikos [email protected] University of Southampton

Ugarte Marıa Dolores [email protected] Public University of Navarre

Uslu Enes Ertad [email protected] Turkish Statistical Institute

Van Den Brakel Jan [email protected] Statistics Netherlands

Van Der Weide Roy [email protected] The World Bank

Wałaszek Monika [email protected] Statistical Office in Krakow

Warnholz Sebastian [email protected] Free University of Berlin

Wawrowski Łukasz [email protected] Poznan University of Economics, Statistical Office in Poznan

Weidenhammer Beate [email protected] Free University of Berlin

Whitworth Alison [email protected] Office for National Statistics

Wieczorek Jerzy [email protected] Carnegie Mellon University

Wilak Kamil [email protected] Poznan University of Economics

Williamson Paul [email protected] University of Liverpool

Witkowski Janusz [email protected] Central Statistical Office

Wójcik Sebastian [email protected] Statistical Office in Rzeszow

Wywiał Janusz [email protected] University of Economics in Katowice

Zhang Junni [email protected] Peking University

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SmallAreaEstimation

2014Zhang Li-Chun [email protected] University of Southampton, Statistics Norway

Żądło Tomasz [email protected] University of Economics in Katowice

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Poland, Poznan, 3–5 September 2014 · Kubacki Jan, Jędrzejczak Alina Small Area Estimation Under...

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