Title:      Sampling Schemes for Generalized Linear Dirichlet
Random Effects Models

Authors:    Minjung Kyung, Jeff Gill, George Casella

Entrydate:  2009-02-18 09:07:16

Keywords:   generalized linear mixed Dirchlet model, Markov
chain Monte Carlo, Dirichlet process priors for random effects,
precision parameters, Scottish Social Attitudes Survey,
terrorism targeting

Abstract:   We evaluate MCMC sampling schemes for a variety of
link functions in generalized linear models with Dirichlet
random effects. We find that models using Dirichlet process
priors for the random effects tend to capture information in the
data in a nonparametric fashion. In fitting the the Dirichlet
process, dealing with the precision parameter has troubled model
specifications in the past. Here we find that incorporating this
parameter into the MCMC sampling scheme is not only
computationally feasible, but also results in a more robust set
of estimates, in that they are marginalized-over rather than
conditioned-upon. Applications are provided with social science
problems in areas where the data can be difficult to model. In
all, we find that these models provide superior Bayesian
posterior results in theory, simulation, and application. 

http://polmeth.wustl.edu/retrieve.php?id=896

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