Title:      Nonparametric Priors For Ordinal Bayesian Social
Science Models: Specification and Estimation

Authors:    Jeff Gill, George Casella

Entrydate:  2008-08-21 22:17:55

Keywords:   generalized linear mixed model, ordered probit,
Bayesian approaches, nonparametric priors, Dirichlet process
mixture models, nonparametric Bayesian inference

Abstract:   A generalized linear mixed model, ordered probit, is
used to estimate levels of stress in presidential political
appointees as a means of understanding their surprisingly short
tenures. A Bayesian approach is developed, where the random
effects are modeled with a Dirichlet process mixture prior,
allowing for useful incorporation of prior information, but
retaining some vagueness in the form of the prior. Applications
of Bayesian models in the social sciences are typically done
with ``noninformative'' priors, although some use of informed
versions exists. There has been disagreement over this, and our
approach may be a step in the direction of satisfying both
camps. We give a detailed description of the data, show how to
implement the model, and describe some interesting conclusions.
The model utilizing a nonparametric prior fits better and
reveals more information in the data than standard approaches.

http://polmeth.wustl.edu/retrieve.php?id=820

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