Statistical Modelling 2 (2002), 183–201
Bayesian spatial models for small area estimation of proportions
Fernando A.S. Moura,
Department of Statistical Methods, Federal University of Rio de Janeiro,
Caixa Postal 68530
BR–21945-970 Rio de Janeiro, RJ,
Brasil
eMail: fmoura@um.ufrj.br
H.S. Migon,
Department of Statistical Methods, Federal University of Rio de Janeiro,
Rio de Janeiro,
Brazil
Abstract:
This article presents a logistic hierarchical model approach for small
area prediction of proportions, taking into account both possible
spatial and unstructured heterogeneity effects. The posterior
distributions of the proportions predictors are obtained via Markov
Chain Monte Carlo methods. This automatically takes into account the
extra uncertainty associated with the hyperparameters. The procedures
are applied to a real data set and comparisons are made under several
settings, including a quite general logistic hierarchical model with
spatial structure plus unstructured heterogeneity for small area
effects. A model selection criterion based on the Expected Prediction
Deviance is proposed. Its utility for selecting among competitive
models in the small area prediction context is examined.
Keywords:
Small area estimation; spatial hierarchical models; model selection;
MCMC; finite population model
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