Modeling regression error with a mixture of Polya trees

• 成果类型: Article
• 署名作者: Hanson, T; Johnson, WO
• 署名单位: University of New Mexico; University of California System; University of California Davis
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214502388618843
• 发表日期: 2002
• 页码: 1020-1033
• 关键词: failure-time model; nonparametric problems; posterior distributions; censored data; inference; survival

摘要

We model the error distribution in the standard linear model as a mixture of absolutely continuous Polya trees constrained to have median 0. By considering a mixture, we smooth out the partitioning effects of a simple Polya tree and the predictive error density has a derivative everywhere except 0. The error distribution is centered around a standard parametric family of distributions and thus may be viewed as a generalization of standard models in which important, data-driven features, such as skewness and multimodality, are allowed. By marginalizing the Polya tree, exact inference is possible up to Markov chain Monte Carlo error.