Penalized Estimation
Penalized Estimation is a statistical inference method that belongs to the class of estimation techniques in which the likelihood function is augmented with a penalty term to regularize parameter estimates, thereby distinguishing it by its ability to simultaneously control model complexity and prevent overfitting in high-dimensional or ill-posed problems.
672
Publications
73.1K
Citations
1.2K
Authors
630
Institutions
Publications per year
2017–2026
170
672
The Adaptive Lasso and Its Oracle Properties
Hui Zou · Journal of the American Statistical Association · 2006 · 7.6K citations
Bias reduction of maximum likelihood estimates
David Firth · Biometrika · 1993 · 4.4K citations
Model selection and estimation in the Gaussian graphical model
Ming Yuan, Yi Lin · Biometrika · 2007 · 1.7K citations
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Centre National de la Recherche Scientifique
Paris, France
19 h-index
38 publications
3.1K citations
North Carolina State University
Raleigh, United States
18 h-index
36 publications
4.5K citations
Stanford University
Stanford, United States
16 h-index
39 publications
3.6K citations
Yale University
New Haven, United States
14 h-index
31 publications
1.8K citations

University of Minnesota
Minneapolis, United States
14 h-index
32 publications
3.1K citations
University of Iowa
Iowa City, United States
13 h-index
18 publications
2.3K citations
Ludwig-Maximilians-Universität München
Munich, Germany
13 h-index
24 publications
1.4K citations
University of Michigan
Ann Arbor, United States
12 h-index
24 publications
901 citations
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53 publications · 12.7K citations
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22 publications · 9.9K citations
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25 publications · 12.6K citations
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Repository · 43 publications · 1.9K citations
19
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25 publications · 1.5K citations
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