Elastic Net Regression

Elastic Net Regression is a regularized linear regression method that combines both L1 and L2 penalty terms to balance sparsity and multicollinearity by simultaneously performing variable selection and coefficient shrinkage.

669

Publications

78.7K

Citations

3.1K

Authors

1.2K

Institutions

Publications per year

2017–2026

471

Parents

Authors

3.1K

  1. HZ
    Hui Zou

    University of Minnesota, United States

    10 publications · 25.4K citations

    10

    h-index

  2. TH
    Trevor Hastie

    Stanford University, United States

    8 publications · 46.5K citations

    8

    h-index

  3. RT
    Robert Tibshirani

    Stanford University, United States

    5 publications · 22.5K citations

    5

    h-index

  4. IC
    Ives Cavalcante Passos

    Universidade Federal do Rio Grande do Sul, Brazil

    5 publications · 174 citations

    5

    h-index

  5. BD
    Bappa Das

    Central Coastal Agricultural Research Institute, India

    4 publications · 153 citations

    4

    h-index

1–5 of 3.1K

Venues

  1. PLoS ONE

    18 publications · 935 citations

    17

    h-index

  2. Scientific Reports

    17 publications · 770 citations

    16

    h-index

  3. BMC Bioinformatics

    11 publications · 668 citations

    11

    h-index

  4. arXiv (Cornell University)

    Repository · 10 publications · 658 citations

    10

    h-index

  5. Bioinformatics

    10 publications · 773 citations

    10

    h-index

1–5