Spring 2010 Thursday, February 11   Speaker: Minjung Kyung, Ph.D. Postdoctoral Fellow Department of Statistics The Universty of Florida Title: "Estimation in Dirichlet Process Random Effects Models" Abstract: We develop a new Gibbs sampler for a linear mixed model with a Dirichlet process random effect term, which is easily extended to a generalized linear mixed model with a probit link function. Our Gibbs sampler exploits the properties of the multinomial and Dirichlet dis- tributions, and is shown to be an improvement, in terms of operator norm and efficiency, over other commonly used MCMC algorithms. We also investigate methods for the estimation of the precision pa- rameter of the Dirichlet process, finding that maximum likelihood may not be desirable, but a posterior mode is a reasonable approach. Examples are given to show how these models perform on real data. Our results complement both the theoretical basis of the Dirichlet process nonparametric prior and the computational work that has been done to date.

Copyright c. 2010 The University of Iowa. All Rights Reserved. Photography c. UI Photo Service and University Relations, University of Iowa This page was last modified on Monday, 08-Feb-2010 08:30:26 CST . /me