Sparse Generalized Linear Models

Sparse Generalized Linear Models is a class of generalized linear models that incorporates regularization or structural assumptions to achieve parsimonious parameter estimates by driving a subset of coefficients to exactly zero, thereby enabling variable selection and model interpretability in high-dimensional settings.

84

Publications

6.4K

Citations

191

Authors

118

Institutions

Publications per year

2017–2026

27

Authors

191

  1. VC
    Victor Chernozhukov

    Massachusetts Institute of Technology, United States

    3 publications · 206 citations

    3

    h-index

  2. AB
    Alexandre Belloni

    Duke University, United States

    3 publications · 206 citations

    3

    h-index

  3. YW
    Ying Wei

    Columbia University, United States

    3 publications · 206 citations

    3

    h-index

  4. MW
    Mingqiu Wang

    Qufu Normal University, China

    2 publications · 52 citations

    2

    h-index

  5. JT
    Jonathan Taylor

    Stanford University, United States

    2 publications · 172 citations

    2

    h-index

1–5 of 191

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 405 citations

    12

    h-index

  2. 6

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 4 publications · 101 citations

    4

    h-index

  4. 4

    h-index

  5. Biometrika

    3 publications · 116 citations

    3

    h-index

1–5