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Activity Number:
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258
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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International Society of Bayesian Analysis
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| Abstract - #307867 |
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Title:
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The Matrix Stick-Breaking Process: Flexible Bayes Meta-Analysis
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Author(s):
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David B. Dunson*+
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Companies:
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National Institute of Environmental Health Sciences
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Address:
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NIH, P.O. Box 12233, MD A3-03, 111 T.W. Alexander Dr, Research Triangle Park, NC, 27709,
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Keywords:
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Meta analysis ; Random effects ; Hierarchical model ; Bayesian ; Nonparametric ; Dirichlet process
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Abstract:
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In analyzing data from multiple related studies, it is often of interest to borrow information across studies and to cluster similar studies. Although parametric hierarchical models are commonly used, a 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 are considered, computational methods are developed, and the approach is illustrated using data from a multinational uterotrophic bioassay study.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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