Bayesian nonparametric inference on stochastic ordering

• 成果类型: Article
• 署名作者: Dunson, David B.; Peddada, Shyamal D.
• 署名单位: Duke University; National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS)
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asn043
• 发表日期: 2008
• 页码: 859874
• 关键词: Consistency; distributions; mixtures; models

摘要

We consider Bayesian inference about collections of unknown distributions subject to a partial stochastic ordering. To address problems in testing of equalities between groups and estimation of group-specific distributions, we propose classes of restricted dependent Dirichlet process priors. These priors have full support in the space of stochastically ordered distributions, and can be used for collections of unknown mixture distributions to obtain a flexible class of mixture models. Theoretical properties are discussed, efficient methods are developed for posterior computation using Markov chain Monte Carlo simulation and the methods are illustrated using data from a study of DNA damage and repair.