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

Authors

2.5K

  1. SM
    Shuangge Ma

    Yale University, United States

    11 publications · 411 citations

    11

    h-index

  2. GT
    Gerhard Tutz

    Ludwig-Maximilians-Universität München, Germany

    11 publications · 901 citations

    11

    h-index

  3. SN
    Simon N. Wood

    University of Bath, United Kingdom

    11 publications · 5.4K citations

    11

    h-index

  4. JH
    Jian Huang

    University of Iowa, United States

    11 publications · 1.3K citations

    11

    h-index

  5. ZY
    Zakariya Yahya Algamal

    University of Mosul, Iraq

    10 publications · 439 citations

    10

    h-index

1–5 of 2.5K

Venues

  1. Journal of the American Statistical Association

    36 publications · 6.8K citations

    31

    h-index

  2. Journal of Computational and Graphical Statistics

    JCGS · 31 publications · 3.7K citations

    25

    h-index

  3. The Annals of Statistics

    27 publications · 8.8K citations

    22

    h-index

  4. arXiv (Cornell University)

    Repository · 32 publications · 1.3K citations

    19

    h-index

  5. Bioinformatics

    21 publications · 2.4K citations

    18

    h-index

1–5