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Abstract Details
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Activity Number:
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47
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Type:
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Invited
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Date/Time:
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Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303609 |
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Title:
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Convergence Analysis of the Gibbs Sampler for Bayesian General Linear Mixed Models with Improper Priors
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Author(s):
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James P. Hobert*+ and Jorge Carlos Roman
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Companies:
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University of Florida and University of Florida
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Address:
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Department of Statistics, Gainesville, FL, 32611,
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Keywords:
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Posterior propriety ;
Geometric ergodicity
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Abstract:
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A popular default prior for the general linear mixed model is an improper prior that takes a product form with a flat prior on the regression parameter, and so-called power priors on each of the variance components. I will describe a convergence rate analysis of the Gibbs samplers associated with these Bayesian models. The main result is a simple, easily-checked sufficient condition for geometric ergodicity of the Gibbs Markov chain.
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