Spike-And-Slab Variable Selection is a Bayesian variable selection method that uses a mixture prior distribution—comprising a "spike" at zero for inactive variables and a "slab" distribution for active coefficients—to probabilistically distinguish relevant from irrelevant predictors in generalized linear models.
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6.5K
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150
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99
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Publications per year
2017–2026
31
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62
Veronika Ročková, Edward I. George · Journal of the American Statistical Association · 2016 · 345 citations · Full text
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Authors
150
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1–5 of 150
Institutions
99
University of Chicago
Chicago, United States
8 h-index
10 publications
548 citations
University of Pennsylvania
Philadelphia, United States
5 h-index
7 publications
820 citations
Kansas State University
Manhattan, United States
4 h-index
11 publications
233 citations
Yale University
New Haven, United States
3 h-index
3 publications
76 citations
University of Florida
Gainesville, United States
3 h-index
4 publications
305 citations
Cleveland Clinic
Cleveland, United States
3 h-index
4 publications
1.2K citations
Case Western Reserve University
Cleveland, United States
2 h-index
5 publications
2.2K citations
University of Alabama at Birmingham
Birmingham, United States
2 h-index
7 publications
309 citations
1–8 of 99
Venues
- Journal of the American Statistical Association
10 publications · 2.4K citations
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- Bayesian Analysis
5 publications · 243 citations
5
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- The Annals of Statistics
3 publications · 1.4K citations
3
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- Statistics in Medicine
3 publications · 72 citations
3
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- Journal of Computational and Graphical Statistics
JCGS · 2 publications · 85 citations
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