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 ********************************************************** Political Methodology E-Mail List Editors: Melanie Goodrich, <[log in to unmask]> Delia Bailey, <[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 **********************************************************