Estimation of the proportion of overweight individuals in small areas - a robust extension of the Fay-Herriot model.
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.
Xie
Dawei
D
Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 617 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA. dxie@cceb.med.upenn.edu
Raghunathan
Trivellore E
TE
Lepkowski
James M
JM
D000293:Adolescent; D040343:Behavioral Risk Factor Surveillance System; D002648:Child; D002675:Child, Preschool; D006801:Humans; D007223:Infant; D015233:Models, Statistical; D016311:Outliers, DRG; D050177:Overweight; D014481:United States