A Bayesian semi-parametric model for small area estimation
Abstract
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.
Cite
@article{arxiv.0805.3264,
title = {A Bayesian semi-parametric model for small area estimation},
author = {Donald Malec and Peter Müller},
journal= {arXiv preprint arXiv:0805.3264},
year = {2008}
}
Comments
Published in at http://dx.doi.org/10.1214/074921708000000165 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)