The matrix stick-breaking process: Flexible Bayes meta-analysis

• 成果类型: Article
• 署名作者: Dunson, David B.; Xue, Ya; Carin, Lawrence
• 署名单位: National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS); General Electric; Duke University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214507000001364
• 发表日期: 2008
• 页码: 317-327
• 关键词: dirichlet processes; models; priors; distributions

摘要

In analyzing data from multiple related studies, it often is of interest to borrow information across studies and to cluster similar studies. Although parametric hierarchical models are commonly used, of concern is sensitivity to the form chosen for the random-effects distribution. A Dirichlet process (DP) prior can allow the distribution to be unknown, while clustering studies; however, the DP does not allow local clustering of studies with respect to a subset of the coefficients without making independence assumptions. Motivated by this problem, we propose a matrix stick-breaking process (MSBP) as a prior for a matrix of random probability measures. Properties of the MSBP are considered, and methods are developed for posterior computation using Markov chain Monte Carlo. Using the MSBP as a prior for a matrix of study-specific regression coefficients, we demonstrate advantages over parametric modeling in simulated examples. The methods are further illustrated using a multinational uterotrophic bioassay study.