Priors For Variable Selection
Priors for Variable Selection is a Bayesian statistical framework that specifies prior distributions over model parameters to facilitate the inclusion or exclusion of predictors in regression models by assigning differential probabilities to variable subsets according to their relevance.
173
Publications
20.6K
Citations
313
Authors
186
Institutions
Publications per year
2017–2026
32
Parents
173
BART: Bayesian additive regression trees
The Annals of Applied Statistics · 2010 · 1.5K citations · Full text
APPROACHES FOR BAYESIAN VARIABLE SELECTION
Edward I. George, Robert E. McCulloch · 1997 · 1.2K citations
Mixtures of <i>g</i> Priors for Bayesian Variable Selection
Feng Liang, Rui Paulo, Germán Molina et al. · Journal of the American Statistical Association · 2008 · 1.1K citations
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University of North Carolina at Chapel Hill, United States
7 publications · 280 citations
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5
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186
Texas A&M University
College Station, United States
8 h-index
13 publications
1.1K citations
Duke University
Durham, United States
7 h-index
8 publications
1.4K citations
University of Chicago
Chicago, United States
7 h-index
8 publications
1.8K citations

University of Warwick
Coventry, United Kingdom
6 h-index
6 publications
833 citations
University of Florida
Gainesville, United States
5 h-index
9 publications
513 citations
University of Kent
Canterbury, United Kingdom
5 h-index
10 publications
1.4K citations

Universidad de Granada
Granada, Spain
5 h-index
6 publications
542 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
5 h-index
13 publications
457 citations
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18 publications · 4.6K citations
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11 publications · 671 citations
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CSDA · 12 publications · 401 citations
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10 publications · 2.7K citations
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JCGS · 5 publications · 130 citations
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