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

Authors

1.2K

  1. SM
    Shuangge Ma

    Yale University, United States

    12 publications · 394 citations

    11

    h-index

  2. JF
    Jianqing Fan

    Princeton University, United States

    11 publications · 3.1K citations

    10

    h-index

  3. JH
    Jian Huang

    University of Iowa, United States

    9 publications · 700 citations

    9

    h-index

  4. HZ
    Hui Zou

    University of Minnesota, United States

    8 publications · 8.4K citations

    8

    h-index

  5. AA
    Anestis Antoniadis

    Université Joseph Fourier, France

    8 publications · 762 citations

    8

    h-index

1–5 of 1.2K

Venues

  1. The Annals of Statistics

    53 publications · 12.7K citations

    38

    h-index

  2. Biometrika

    22 publications · 9.9K citations

    22

    h-index

  3. Journal of the American Statistical Association

    25 publications · 12.6K citations

    20

    h-index

  4. arXiv (Cornell University)

    Repository · 43 publications · 1.9K citations

    19

    h-index

  5. Electronic Journal of Statistics

    25 publications · 1.5K citations

    17

    h-index

1–5