Author(s): Xie D, Raghunathan TE, Lepkowski JM
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Abstract 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 FH 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. (c) 2006 John Wiley & Sons, Ltd.
This article was published in Stat Med
and referenced in Journal of Aquaculture Research & Development