Model Selection

Model Selection is the process of identifying the most appropriate statistical or mathematical framework among a set of candidate models that best balances explanatory power, predictive accuracy, and parsimony for a given dataset and research question within a specified modeling domain.

3.4K

Publications

527.8K

Citations

6.6K

Authors

2.2K

Institutions

Publications per year

2017–2026

813

Authors

6.6K

  1. CT
    Chih‐Ling Tsai

    University of California, Davis, United States

    22 publications · 9.4K citations

    19

    h-index

  2. DF
    David F. Hendry

    University of Oxford, United Kingdom

    22 publications · 2K citations

    18

    h-index

  3. RL
    Runze Li

    Pennsylvania State University, United States

    18 publications · 2.4K citations

    16

    h-index

  4. YY
    Yuhong Yang

    University of Minnesota, United States

    17 publications · 2.1K citations

    15

    h-index

  5. RT
    Robert Tibshirani

    Stanford University, United States

    15 publications · 19.7K citations

    14

    h-index

1–5 of 6.6K

Venues

  1. Journal of the American Statistical Association

    120 publications · 26.4K citations

    69

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 83 publications · 4.7K citations

    38

    h-index

  3. The Annals of Statistics

    60 publications · 19.6K citations

    38

    h-index

  4. Biometrika

    60 publications · 15.9K citations

    36

    h-index

  5. Technometrics

    53 publications · 17K citations

    34

    h-index

1–5