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Activity Number: 623 - Statistical Modeling: Benefits and Drawbacks
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #306958
Title: A Reconsideration of the Gibbs Sampler for Small Area Estimation Models
Author(s): William Bell*
Companies: U.S. Census Bureau
Keywords: Markov Chain Monte Carlo; Bayesian computation; BUGS; JAGS

The substantial development of Bayesian inference over recent decades has owed much to the development of Markov Chain Monte Carlo algorithms, for which the Gibbs sampler has played a pivotal role. The Gibbs sampler has been popular for Bayesian treatment of linear small area estimation models, such as the Fay-Herriot model, for which it seems to be a natural fit. Relatively little attention seems to have been paid, however, to how well the Gibbs sampler performs for such models. We will examine the Gibbs sampler for Fay-Herriot models and show via numerical illustrations that its performance can be quite poor, and that much better alternatives are readily available.

Authors who are presenting talks have a * after their name.

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