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

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