Cross-validated Model Averaging

Cross-Validated Model Averaging is a model averaging technique that assigns weights to candidate models based on their predictive performance evaluated through cross-validation, thereby ensuring robustness against overfitting by leveraging out-of-sample accuracy in the weighting scheme.

5

Publications

73

Citations

13

Authors

12

Institutions

Publications per year

2017–2026

4

Authors

13

  1. AP
    Achim Pascal Meyer

    Bernstein Center for Computational Neuroscience Berlin, Germany

    1 publications · 12 citations

    1

    h-index

  2. YW
    Yuting Wei

    Nankai University, China

    1 publications · 11 citations

    1

    h-index

  3. JH
    John­–Dylan Haynes

    Bernstein Center for Computational Neuroscience Berlin, Germany

    1 publications · 12 citations

    1

    h-index

  4. XZ
    Xinyu Zhang

    Chinese Academy of Sciences, China

    1 publications · 16 citations

    1

    h-index

  5. JC
    Jeng‐Min Chiou

    Institute of Statistical Science, Academia Sinica, Taiwan

    1 publications · 16 citations

    1

    h-index

1–5 of 13

Venues

  1. ACM SIGKDD Explorations Newsletter

    1 publications · 22 citations

    1

    h-index

  2. Statistics & Probability Letters

    1 publications · 11 citations

    1

    h-index

  3. Biometrika

    1 publications · 16 citations

    1

    h-index

  4. NeuroImage

    1 publications · 12 citations

    1

    h-index

  5. Econometrics

    1 publications · 12 citations

    1

    h-index

1–5