Alan E. Gelfand is the JamesB. Duke Professor of StatisticalScience at Duke University.He is Chair of the Departmentof Statistical Science andProfessor of EnvironmentalScience. Author of 200 pluspapers (more than 70 in thearea of spatial statistics), he isinternationally known for hiscontributions to applied statis-

tics, Bayesian computation, and Bayesian inference. Heis an elected fellow of the American StatisticalAssociation and the Institute of MathematicalStatistics, and an elected member of theInternational Statistical Institute. ProfessorGelfand is a former president of theInternational Society for BayesianAnalysis and in 2006 he received theParzen Prize for a lifetime of research con-tribution to statistics. His primary researchfocus for the past 13 years has been in thearea of statistical modeling for spatial andspace-time data. A frequent contributor to CRCPress publications, he is co-author of the bestsellingHierarchical Modeling and Analysis for Spatial Data(new edition scheduled for 2011). He is also lead editorfor the newly released Handbook of Spatial Statistics(March 2010).

Through a collection of more than 60 papers ProfessorGelfand has advanced methodology, using the Bayesianparadigm, to associate fully model-based inference withspatial and space-time data displays. His chief areas of application include environmental exposure, spatio-temporal ecological processes, and climatologicalmodeling.

Now chair of Columbia University’s highlyrespected Department of Statistics, DavidMadigan uses his position to strongly advo-cate for the field of statistics validity as aversatile and independent science. Sincereceiving his Ph.D. from Trinity College inDublin, Madigan has proven himself a pro-lific and ardent researcher, first at theUniversity of Washington and then atRutgers. Working for such companies asAT&T Inc., Soliloquy Inc., and SkillSoft,Inc, provided him with the opportunities to apply his science to

modern problem solving. Decidedly Bayesian in his approach,he has over 100 publishing credits writing about a num-ber of ways statistics intersects with other fields, includ-ing drug discovery and wireless technology. ProfessorMadigan is an elected Fellow of the AmericanStatistical Association and the Institute ofMathematical Statistics. He is the current Editor-in-Chief of Statistical Science, a peer review journal pub-lished by the Institute of Mathematical Statistics. He is also the editor of the Chapman & Hall/CRC

Computer Science and Data Analysis Series.

Madigan stresses the idea that statistics is no more a branch ofmathematics than any other field that uses mathematical tools. Hebelieves that the field should be producing outstanding scientists aswell as outstanding mathematicians. While he recognizes that somespecialization is of value, Professor Madigan has stated that special-ization along applied versus theoretical lines does a disservice to thescience, as that distinction reinforces the concept of a theoreticalstatistician developing mathematical artifacts without reference toany scientific enquiry, while the lower-browed applied statisticianconducts the intellectually less rigorous job of implementing theo-ry. For the professor, the complete statistician must concern him orherself with both, and fortunately, students at Columbia are provid-ed with an excellent role model of such a scientist.

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StatisticsNewS What’s News StatisticsNewS PioneersPlotting the Future

SATURDAY, JULY 31

Monte Carlo and Bayesian Computation with R (CE_03C)Section for Statistical Programmers and Analysts, Section on BayesianStatistical Science4 Maria Rizzo, author of Statistical Computing with R teaches thiscourse with Jim Albert, co-editor of Statistical Thinking in Sports.

Causal Inference (CE_01C)4 Miguel Hernan and James Robins, co-authors of the forthcoming

Causal Inference, teach this course.

SUNDAY, AUGUST 1

Statistical Issues in Approval of Follow-on Biologics - Invited –Papers 47Biopharmaceutical Section, ENAR4 Shein-Chung Chow, series editor for Chapman & Hall/CRC

Biostatistics Series, chairs this session.

Bayesian Ecology: Hierarchical Modeling for EcologicalProcesses (CE_10C)4 Instructed by Alan E. Gelfand, co-author of Handbook of Spatial

Statistics and Hierarchical Modeling and Analysis for SpatialData.

Statistical Methods for Spatial Longitudinal/Functional Data -Invited – Papers 52Section on Statistical Computing, IMS, International Chinese StatisticalAssociation, Section on Nonparametric Statistics, Section on Physical andEngineering Sciences, Section on Statistics and the Environment, WNAR4 Sudipto Banerjee, co author of Hierarchical Modeling and

Analysis for Spatial Data and Linear Algebra and MatrixComputations for Statistics, presents Hierarchical Spatial Models forPredicting Forest Variables over Large Heterogeneous Domains withAndrew Finley.

MONDAY, AUGUST 2

Medallion Lecture - Invited – Papers 146IMS, International Chinese Statistical Association4 Xiao-Li Meng, co-editor of Handbook of Markov Chain Monte

Carlo: Methods and Applications, presents What Can We DoWhen EM Is Not Applicable? Self Consistency: A General Recipe forSemi-parametric and Non-parametric Estimation with Incomplete andIrregularly Spaced Data.

JASA, Theory and Methods Invited Session - Invited – Papers152JASA, Theory and Methods4 Bradley Efron, co-author of An Introduction to the Bootstrap,presents Correlated z-values and the Accuracy of Large-scale StatisticalEstimates.

TUESDAY, AUGUST 3

Analysis of Longitudinal Data Using Antedependence Models(CE_17C)4 Instructed by Dale Zimmerman, co-author of Antedependence

Models for Longitudinal Data.

Bayesian Adaptive Methods for Clinical Trials (CE_19C)4 Taught by Bradley P. Carlin, Scott Berry, and J. Jack Lee, co-authors of Bayesian Adaptive Methods for Clinical Trials, withDonald Berry, co-authors of Bayesian Biostatistics

Advances in Functional Data Analysis - Invited – Papers 272Section on Nonparametric Statistics4 Raymond Carroll, co-author of Measurement Error in Nonlinear

Models: A Modern Perspective, Second Edition presentsGeneralized Functional Latent Feature Models with Single-IndexInteractions with Yehua Li and Naisyin Wang. His co-authorCiprian Crainiceanu presents Longitudinal Functional PrincipalComponent Analysis.

Statistical Analysis of Complex Networks - a SAMSI Preview -Invited – Papers 384Section on Statistical Computing4 Mike West, author of Time Series: Modeling, Computation, and

Inference examines Issues in Model Emulation/Evaluation inDynamic Network Studies in Systems Biology.

Statistical Methods Used in Defense and Non-defenseApplications - Invited – Panel 326Section on Statistics in Defense and National Security, InternationalChinese Statistical Association4 Max Morris, author of Design of Experiments: An Introduction

Based on Linear Models presents Statistical Methods Used inDefense and Non-defense Applications with collaborators.

WEDNESDAY, AUGUST 4

Graphics Packages for R, Recent Advances and FutureDirections - Invited – Papers 446Section on Statistical Graphics, Committee on Applied Statisticians, Sectionfor Statistical Programmers and Analysts, Section on Government Statistics,Section on Statistical Computing4 Organized and chaired by Daniel B. Carr, co-author of Visualizing

Data Patterns with Micromaps.Section on Survey Research Methods PM Roundtable Discussion (fee event) 5494 Join Brady West, co-author of Applied Survey Data Analysis and

Linear Mixed Models: A Practical Guide Using StatisticalSoftware in a discussion about Fitting Multilevel Models to ComplexSample Survey.

THURSDAY, AUGUST 5

Key Multiplicity Issues in Clinical Trials - Invited – Papers 645Biopharmaceutical Section, Committee on Applied Statisticians, ENAR4 Organized by Alex Dmitrienko, co-editor of Multiple Testing

Problems in Pharmaceutical Statistics.

Bayesian Nonparametric Modeling of Longitudinal andSurvival Data - Invited – Papers 553Section on Nonparametric Statistics, Business and Economic StatisticsSection, IMS, Section on Bayesian Statistical Science, Section on HealthPolicy Statistics, Section on Risk Analysis, WNAR4 Wesley Johsnon, co-author of Bayesian Ideas and Data Analysis:

An Introduction for Scientists and Statisticians presents BayesianNonparametric Longitudinal Data Analysis with Embedded AutoregressiveStructure: Application to Hormone Data with Fernando Quintana.

Effective Use of Instructional Technology - Invited – Papers 647Section on Statistical Education, Section on Statistical Computing4 Nicholas Jon Horton, co-author of SAS and R: Data

Management, Statistical Analysis, and Graphics and Using R forData Management, Statistical Analysis, and Graphics, presentsGuiding Student Project Workflow Using Reproducible StatisticalAnalysis Tools.

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◆ ◆ ◆ JSM HIGHLIGHTS ◆ ◆ ◆

Observed Confidence Levels, Alan M. Polansky

Analysis of Correlated Data with SAS and R, Third Edition, Mohamed M. Shoukri and Mohamed A. Chaudhary

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