Hierarchical model such as Fay-Herriot (FH) model is often used in small area estimation. The method might perform well overall but is vulnerable to outliers. We propose a robust extension of the FE model by assuming the area random effects follow a t distribution with an unknown degrees-of-freedom parameter. The inferences are constructed using a Bayesian framework. Monte Carlo Markov Chain (MCMC) such as Gibbs sampling and Metropolis-Hastings acceptance and rejection algorithms are used to obtain the joint posterior distribution of model parameters. The procedure is used to estimate the county-level proportion of overweight individuals from the 2003 public-use Behavioral Risk Factor Surveillance System (BRFSS) data. We also discuss two approaches for identifying outliers in the context of this application. Copyright © 2006 John Wiley & Sons, Ltd.
CITATION STYLE
Xie, D., Raghunathan, T. E., & Lepkowski, J. M. (2007). Estimation of the proportion of overweight individuals in small areas - A robust extension of the Fay-Herriot model. Statistics in Medicine, 26(13), 2699–2715. https://doi.org/10.1002/sim.2709
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