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

Authors

1.3K

  1. JA
    Jeffrey A. Fessler

    University of Michigan, United States

    15 publications · 2.1K citations

    13

    h-index

  2. CG
    Chong Gu

    Purdue University West Lafayette, United States

    13 publications · 642 citations

    12

    h-index

  3. JF
    Jianqing Fan

    Princeton University, United States

    12 publications · 13.1K citations

    12

    h-index

  4. PJ
    Pierre Joly

    Inserm, France

    12 publications · 788 citations

    11

    h-index

  5. DC
    Daniel Commenges

    Inserm, France

    11 publications · 610 citations

    10

    h-index

1–5 of 1.3K

Venues

  1. Journal of the American Statistical Association

    44 publications · 15K citations

    32

    h-index

  2. The Annals of Statistics

    39 publications · 6.6K citations

    31

    h-index

  3. Statistics in Medicine

    38 publications · 7.2K citations

    26

    h-index

  4. Biometrics

    39 publications · 2.4K citations

    25

    h-index

  5. IEEE Transactions on Medical Imaging

    20 publications · 2.1K citations

    19

    h-index

1–5