Abstract #300804

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JSM 2003 Abstract #300804
Activity Number: 366
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300804
Title: Exact Simulation for a Class of Bayesian Variance Component Models
Author(s): Murali Haran*+
Companies: University of Minnesota
Address: School of Stats, Minneapolis, MN, 55455,
Keywords: Monte Carlo ; rejection sampling ; perfect simulation ; Bayesian hierarchical model
Abstract:

We outline a systematic, easy to use method for producing independent draws from posterior distributions for a class of popular Bayesian normal linear mixed models. Inference for such models is generally carried out using Markov chain Monte Carlo methods, which produce dependent samples. We exploit the low dimensionality of the variance components in these models to obtain suitable proposal distribution for a rejection sampling algorithm. We illustrate the application of our approach with several examples. We find that our methods are easy to use and produce i.i.d. draws fairly efficiently, thereby simplifying the calculation of Monte Carlo standard errors, and avoiding issues of Markov chain convergence diagnosis. Time permitting, we will also explain how perfect tempering algorithms (as described in Moller and Nicholls, 1999) can be used to simulate from these posterior distributions.


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