Open Access
VOL. 3 | 2008 A Bayesian semi-parametric model for small area estimation
Donald Malec, Peter Müller

Editor(s) Bertrand Clarke, Subhashis Ghosal

Inst. Math. Stat. (IMS) Collect., 2008: 223-236 (2008) DOI: 10.1214/074921708000000165


In public health management there is a need to produce subnational estimates of health outcomes. Often, however, funds are not available to collect samples large enough to produce traditional survey sample estimates for each subnational area. Although parametric hierarchical methods have been successfully used to derive estimates from small samples, there is a concern that the geographic diversity of the U.S. population may be oversimplified in these models. In this paper, a semi-parametric model is used to describe the geographic variability component of the model. Specifically, we assume Dirichlet process mixtures of normals for county-specific random effects. Results are compared to a parametric model based on the base measure of the Dirichlet process, using binary health outcomes related to mammogram usage.


Published: 1 January 2008
First available in Project Euclid: 28 April 2008

MathSciNet: MR2459227

Digital Object Identifier: 10.1214/074921708000000165

Primary: 62-07 , 62G07

Keywords: Dirichlet process , Mixture models , National Health Interview Survey

Rights: Copyright © 2008, Institute of Mathematical Statistics

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