Abstract #301481

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JSM 2003 Abstract #301481
Activity Number: 199
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301481
Title: Large Sample Results for the Linear Mixed Models and Their Applications
Author(s): Chun-Lung Su*+
Companies:
Address: 803 S Manley Dr., San Gabriel, CA, 91776-2326,
Keywords: linear mixed model ; asymptotic ; sufficient reduction ; convergence rate
Abstract:

Modern Bayesian statistical methods such as Gibbs and Metropolis-Hastings sampling can result in slow and/or inefficient MCMC methods for large samples. Yee, Johnson and Samaniego (2002) developed a method for constructing asymptotic posterior approximations that addressed this problem. They considered situations where conditional distributions for two blocks of random variables have asymptotic normal distributions. We generalized the YJS results up to k blocks. We then proceed to apply these when considering the asymptotic behavior of posteriors for parameters of linear mixed models (LMM's). We also consider the relevance of different parameterizations with regard to our asymptotics for the one-way random effects model. We compare these different parameterizations results based on simulated data when n or k is large. A dataset involving the effect of smoking on hormone function is analyzed by using our asymptotics and is compared with results based on Gibbs sampling.

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