Cross-validation Studies

Cross-Validation Studies is a methodological framework in computational statistics that systematically partitions genomic and nonparametric data into complementary subsets to evaluate the predictive performance and generalizability of statistical models by iteratively training and testing across different folds.

1.4K

Publications

141.9K

Citations

4.9K

Authors

1.8K

Institutions

Publications per year

2017–2026

438

Authors

4.9K

  1. DC
    Daniel Cozzolino

    Australian Wine Research Institute, Australia

    12 publications · 722 citations

    12

    h-index

  2. MS
    M. Stone

    University College London, United Kingdom

    7 publications · 13.6K citations

    7

    h-index

  3. AB
    Anne‐Laure Boulesteix

    Ludwig-Maximilians-Universität München, Germany

    6 publications · 268 citations

    6

    h-index

  4. PH
    Peter Hall

    Australian National University, Australia

    5 publications · 467 citations

    5

    h-index

  5. PB
    Prabir Burman

    University of California, Davis, United States

    4 publications · 923 citations

    4

    h-index

1–5 of 4.9K

Venues

  1. Bioinformatics

    27 publications · 2.1K citations

    24

    h-index

  2. PLoS ONE

    27 publications · 1.5K citations

    20

    h-index

  3. PubMed

    Repository · 22 publications · 3.9K citations

    16

    h-index

  4. Journal of the American Statistical Association

    18 publications · 4.2K citations

    15

    h-index

  5. BMC Bioinformatics

    17 publications · 997 citations

    15

    h-index

1–5