Regularized Regression Models
Regularized Regression Models is a class of statistical regression techniques that incorporates penalty terms to constrain model complexity, thereby improving generalization and variable selection in high-dimensional genomic data.
654
Publications
47.8K
Citations
2.1K
Authors
852
Institutions
Publications per year
2017–2026
299
Variants
Penalized Regression Models
Parents
654
Computationally efficient whole-genome regression for quantitative and binary traits
Joelle Mbatchou, Leland Barnard, Joshua Backman et al. · Nature Genetics · 2021 · 1.6K citations
Noah Simon, Jerome H. Friedman, Trevor Hastie et al. · Journal of Computational and Graphical Statistics · 2012 · 1.4K citations
Group lasso with overlap and graph lasso
Laurent Jacob, Guillaume Obozinski, Jean‐Philippe Vert · 2009 · 868 citations
1–5 of 654
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852
Stanford University
Stanford, United States
24 h-index
110 publications
34.5K citations
Carnegie Mellon University
Pittsburgh, United States
13 h-index
33 publications
3.1K citations
University of Michigan
Ann Arbor, United States
13 h-index
30 publications
7K citations
University of California, Berkeley
Berkeley, United States
11 h-index
26 publications
1.4K citations
Columbia University
New York, United States
11 h-index
21 publications
833 citations
University of Chicago
Chicago, United States
10 h-index
14 publications
690 citations
Massachusetts Institute of Technology
Cambridge, United States
10 h-index
21 publications
488 citations
Ludwig-Maximilians-Universität München
Munich, Germany
9 h-index
19 publications
972 citations
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Repository · 37 publications · 2.2K citations
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20 publications · 1.8K citations
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13 publications · 455 citations
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13 publications · 537 citations
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CSDA · 12 publications · 500 citations
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