Bayesian hierarchical models for demographic small area estimation John Bryant Statistics New...
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![Page 1: Bayesian hierarchical models for demographic small area estimation John Bryant Statistics New Zealand September 2013.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697c0211a28abf838cd2f32/html5/thumbnails/1.jpg)
Bayesian hierarchical models for demographic small area estimation
John Bryant
Statistics New Zealand
September 2013
![Page 2: Bayesian hierarchical models for demographic small area estimation John Bryant Statistics New Zealand September 2013.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697c0211a28abf838cd2f32/html5/thumbnails/2.jpg)
Examples of demographic small area estimation
Birth rates by age of mother by ‘area unit’• 39 age groups• 70+ territorial authorities• 61,000 births
Maori deaths by age and sex• 101 age groups• 2 sexes• 3,000 deaths
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Characteristics of demographic data
Cross-classified counts• Not records × variables
Often ‘complete’ counts rather than survey
Time-varying
Strong regularities
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Deaths, Maori males
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Bayesian hierarchical models an attractive approach
Demographic data are hierarchical
Shrinkage
Flexibility
Forecasting, probabilistic statements
Recent surge in number of papers
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Packages Demographic and DemographicEstimation
Under development
Originally only ‘Demographic accounts’• later realized more general application
Demographicdata structures and basic manipulation functions
DemographicEstimationBayesian hierarchical models, customised for demographic problems
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Application: Births rates by small area
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Region 13, 1996 = 11 births; Region 2, 2006 = 1490 births; 10% of cells missing
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A model, three ways
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res <- estimateModel(Model(y ~ Poisson(mean ~ age * region + year), region ~ Exch(mean ~ income + propn.maori, data = data.reg)), y = births, exposure = deaths, file = "fertility.res")
(1) (2)
(3)
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Results: All regions and years
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theta <- fetch(res, where = c("model", "likelihood", "mean"))p <- dplot(~ age | region + factor(year), data = theta, midpoints = "age")useOuterStrips(p)
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Results, with unsmoothed rates
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Results: Change over time
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regions <- paste("Reg", c(2, 5, 8, 13))p <- dplot(~ year | factor(age) * region, data = theta, subarray = region %in% regions, weights = females, overlay = list(values = subarray(births/females, region %in% regions), pch = 19, col = "black"))useOuterStrips(p)
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Results: Covariates
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covariate <- fetch(res, where = c("model", "hyper", "region", "covariates"))dplot(~ covariate, data = covariate)
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Other features
Normal and binomial models
Diagnostics• Convergence• Replicate data
Manipulation of (voluminous) output
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Future work
More priors
Survey data
Forecasting
Lots more testing• Especially on big datasets
Eventually release on CRAN
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