Inference for mixtures of finite Polya tree models

• 成果类型: Article
• 署名作者: Hanson, Timothy E.
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214506000000384
• 发表日期: 2006
• 页码: 1548-1565
• 关键词: bayesian density-estimation; generalized linear-models; chain monte-carlo; marginal likelihood; regression-models; posterior distribution; nonparametric problems; Dirichlet process; distributions; survival

摘要

Mixtures of Polya tree models provide a flexible alternative when a parametric model may only hold approximately. I provide computational strategies for obtaining full serniparametric inference for mixtures of finite Polya tree models given a standard parameterization, including models that would be troublesome to fit using Dirichlet process mixtures. Recommendations are put forth on choosing the level of a finite Polya tree, and model comparison is discussed. Several examples demonstrate the utility of finite Polya tree modeling, including data fit to generalized linear mixed models and several survival models.