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

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