Title: Estimation in Dirichlet Random Effects Models Authors: Jeff Gill, George Casella, Minjung Kyung Entrydate: 2009-04-28 13:47:20 Keywords: generalized linear mixed model, Dirichlet process random effects model, precision parameter likelihood, Gibbs sampling, importance sampling, probit mixed Dirichlet random effects model 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 distribution, 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 parameter 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. http://polmeth.wustl.edu/retrieve.php?id=901 ********************************************************** Political Methodology E-Mail List Editors: Melanie Goodrich, <[log in to unmask]> Xun Pang, <[log in to unmask]> ********************************************************** Send messages to [log in to unmask] To join the list, cancel your subscription, or modify your subscription settings visit: http://polmeth.wustl.edu/polmeth.php **********************************************************