Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 20, Issue 1, Pages 80-101Publisher
AMER STATISTICAL ASSOC
DOI: 10.1198/jcgs.2010.09049
Keywords
Bayesian model averaging; Inclusion probability; Markov chain Monte Carlo; Median probability model; Model uncertainty; Sampling without replacement
Categories
Funding
- National Science Foundation [DMS-9733013, DMS-0422400, AST-0507481]
- National Institutes of Health [1R01-HL-090559, NIH/NIEHS 5T32ES007018]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1018152] Funding Source: National Science Foundation
Ask authors/readers for more resources
For the problem of model choice in linear regression, we introduce a Bayesian adaptive sampling algorithm (BAS), that samples models without replacement from the space of models. For problems that permit enumeration of all models, BAS is guaranteed to enumerate the model space in 2(P) iterations where p is the number of potential variables under consideration. For larger problems where sampling is required, we provide conditions under which BAS provides perfect samples without replacement. When the sampling probabilities in the algorithm are the marginal variable inclusion probabilities, BAS may be viewed as sampling models near the median probability model of Barbieri and Berger. As marginal inclusion probabilities are not known in advance, we discuss several strategies to estimate adaptively the marginal inclusion probabilities within BAS. We illustrate the performance of the algorithm using simulated and real data and show that BAS can outperform Markov chain Monte Carlo methods. The algorithm is implemented in the R package BAS available at CRAN. This article has supplementary material online.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available