Multilevel Variable Selection
Multilevel Variable Selection is a statistical methodology within Bayesian multilevel modeling that systematically identifies and retains relevant predictors at multiple hierarchical levels by jointly estimating their inclusion probabilities while accounting for nested data structures and random effects.
71
Publications
6.3K
Citations
216
Authors
121
Institutions
Publications per year
2017–2026
22
Parents
71
Clustering and Classification.
Phipps Arabie, Lawrence J. Hubert, G. De Soete · Biometrics · 1997 · 416 citations
Gene selection using a two-level hierarchical Bayesian model
Kyounghwa Bae, Bani K. Mallick · Bioinformatics · 2004 · 174 citations
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121

Centre National de la Recherche Scientifique
Paris, France
3 h-index
6 publications
366 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
2 h-index
6 publications
95 citations

Western University
London, Canada
2 h-index
6 publications
480 citations
University of Copenhagen
Copenhagen, Denmark
2 h-index
4 publications
244 citations
Equinor (Norway)
Stavanger, Norway
2 h-index
2 publications
367 citations

University of Rochester
Rochester, United States
2 h-index
6 publications
144 citations

Lurie Children's Hospital
Chicago, United States
2 h-index
2 publications
297 citations
Highland Community College - Illinois
Freeport, United States
2 h-index
2 publications
367 citations
1–8 of 121
4 publications · 2.2K citations
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CSDA · 4 publications · 166 citations
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3 publications · 68 citations
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2 publications · 108 citations
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2 publications · 433 citations
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