Regularized Regression Models

Regularized Regression Models is a class of statistical regression techniques that incorporates penalty terms to constrain model complexity, thereby improving generalization and variable selection in high-dimensional genomic data.

654

Publications

47.8K

Citations

2.1K

Authors

852

Institutions

Publications per year

2017–2026

299

Variants

Penalized Regression Models

Authors

2.1K

  1. TH
    Trevor Hastie

    Stanford University, United States

    12 publications · 3.9K citations

    12

    h-index

  2. RT
    Robert Tibshirani

    Stanford University, United States

    10 publications · 5K citations

    10

    h-index

  3. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    7 publications · 679 citations

    7

    h-index

  4. DC
    Di‐Rong Chen

    Beihang University, China

    5 publications · 149 citations

    5

    h-index

  5. GT
    Gerhard Tutz

    Ludwig-Maximilians-Universität München, Germany

    5 publications · 262 citations

    5

    h-index

1–5 of 2.1K

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 2.2K citations

    20

    h-index

  2. Bioinformatics

    20 publications · 1.8K citations

    17

    h-index

  3. BMC Bioinformatics

    13 publications · 455 citations

    12

    h-index

  4. PLoS ONE

    13 publications · 537 citations

    12

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 12 publications · 500 citations

    12

    h-index

1–5