Regularized Estimation

Regularized Estimation is a parameter estimation method that incorporates a penalty term into the estimation criterion to constrain model complexity and prevent overfitting by biasing estimates toward simpler solutions.

926

Publications

62.2K

Citations

1.9K

Authors

800

Institutions

Publications per year

2017–2026

257

Authors

1.9K

  1. MY
    Ming Yuan

    Georgia Institute of Technology, United States

    9 publications · 2.5K citations

    9

    h-index

  2. TC
    Tianshi Chen

    Linköping University, Sweden

    9 publications · 312 citations

    9

    h-index

  3. LL
    Lennart Ljung

    Linköping University, Sweden

    9 publications · 414 citations

    9

    h-index

  4. MJ
    Martin J. Wainwright

    University of California, Berkeley, United States

    8 publications · 1K citations

    8

    h-index

  5. JF
    Jianqing Fan

    Princeton University, United States

    7 publications · 1.1K citations

    7

    h-index

1–5 of 1.9K

Venues

  1. arXiv (Cornell University)

    Repository · 82 publications · 3.2K citations

    27

    h-index

  2. The Annals of Statistics

    29 publications · 5.2K citations

    24

    h-index

  3. IEEE Transactions on Signal Processing

    29 publications · 1.9K citations

    21

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 17 publications · 480 citations

    14

    h-index

  5. Neurocomputing

    13 publications · 659 citations

    13

    h-index

1–5