Penalized Regression
Penalized Regression is a regression method that belongs to the class of variable selection techniques by imposing a penalty on the magnitude of coefficients to simultaneously achieve model parsimony and prevent overfitting.
842
Publications
70.5K
Citations
2.5K
Authors
1.1K
Institutions
Publications per year
2017–2026
281
Variants
Shrinkage Regression
Parents
842
1–5 of 842
2.5K
11
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10
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1–5 of 2.5K
1.1K

University of Minnesota
Minneapolis, United States
21 h-index
59 publications
3.6K citations
University of Iowa
Iowa City, United States
19 h-index
30 publications
3.6K citations
Harvard University
Cambridge, United States
18 h-index
55 publications
8.3K citations
Ludwig-Maximilians-Universität München
Munich, Germany
17 h-index
39 publications
4.1K citations
Yale University
New Haven, United States
17 h-index
34 publications
3K citations

Centre National de la Recherche Scientifique
Paris, France
16 h-index
45 publications
2.3K citations
Stanford University
Stanford, United States
15 h-index
32 publications
8.7K citations
North Carolina State University
Raleigh, United States
15 h-index
25 publications
3.7K citations
1–8 of 1.1K
36 publications · 6.8K citations
31
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JCGS · 31 publications · 3.7K citations
25
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27 publications · 8.8K citations
22
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Repository · 32 publications · 1.3K citations
19
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21 publications · 2.4K citations
18
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