Nonparametric Priors for Ordinal Bayesian Social Science Models: Specification and Estimation

• 成果类型: Article
• 署名作者: Gill, Jeff; Casella, George
• 署名单位: Washington University (WUSTL); State University System of Florida; University of Florida
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0039
• 发表日期: 2009
• 页码: 453-464
• 关键词: chain monte-carlo; international-relations; political executives; dirichlet processes; inference; parameters; likelihood; mixtures; distributions; simulation

摘要

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 uninformative 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.