Penalized Likelihood
Penalized Likelihood is a statistical inference method that modifies the standard likelihood function by incorporating a penalty term to constrain model complexity, thereby distinguishing it from unpenalized likelihood estimation through the explicit incorporation of prior information or regularization to prevent overfitting.
651
Publications
84.5K
Citations
1.3K
Authors
644
Institutions
Publications per year
2017–2026
119
Parents
651
Bias reduction of maximum likelihood estimates
David Firth · Biometrika · 1993 · 4.4K citations
Flexible smoothing with B-splines and penalties
Paul H.C. Eilers, Brian D. Marx · Statistical Science · 1996 · 3.7K citations · Full text
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University of Michigan
Ann Arbor, United States
25 h-index
69 publications
8K citations
University of Wisconsin–Madison
Madison, United States
19 h-index
41 publications
4.3K citations
Inserm
Paris, France
16 h-index
47 publications
2.7K citations
University of California, Berkeley
Berkeley, United States
15 h-index
16 publications
2.5K citations
University of Washington
Seattle, United States
15 h-index
22 publications
3.9K citations

Centre National de la Recherche Scientifique
Paris, France
13 h-index
19 publications
1.6K citations
Bordeaux Population Health
Bordeaux, France
12 h-index
26 publications
1.6K citations
Harvard University
Cambridge, United States
12 h-index
22 publications
583 citations
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44 publications · 15K citations
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