Title: Characterizing the variance improvement in linear Dirichlet random effects models Authors: Minjung Kyung, Jeff Gill, George Casella Entrydate: 2009-09-11 08:28:24 Keywords: Dirichlet processes, mixture models, Bayesian nonparametrics Abstract: An alternative to the classical mixed model with normal random effects is to use a Dirichlet process to model the random effects. Such models have proven useful in practice, and we have observed a noticeable variance reduction, in the estimation of the fixed effects, when the Dirichlet process is used instead of the normal. In this paper we formalize this notion, and give a theoretical justification for the expected variance reduction. We show that for almost all data vectors, the posterior variance from the Dirichlet random effects model is smaller than that form the normal random effects model. Forthcoming: Statistics and Probability Letters http://polmeth.wustl.edu/retrieve.php?id=936 ********************************************************** Political Methodology E-Mail List Editors: Xun Pang <[log in to unmask]> Jon C. Rogowski <[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 **********************************************************