Cross-validated Model Selection

Cross-Validated Model Selection is a model selection procedure that belongs to the class of resampling techniques and is distinguished by its use of cross-validation to estimate the predictive performance of candidate models, thereby enabling the selection of the optimal model based on generalization accuracy.

76

Publications

15.4K

Citations

163

Authors

116

Institutions

Publications per year

2017–2026

16

Authors

163

  1. JS
    Joram Soch

    Bernstein Center for Computational Neuroscience Berlin, Germany

    3 publications · 102 citations

    3

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  2. ML
    Mikio L. Braun

    Technische Universität Berlin, Germany

    2 publications · 125 citations

    2

    h-index

  3. PE
    P. Eykhoff

    Eindhoven University of Technology, Netherlands

    2 publications · 142 citations

    2

    h-index

  4. WR
    William R. Schucany

    Southern Methodist University, United States

    2 publications · 37 citations

    2

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  5. JH
    John­–Dylan Haynes

    Bernstein Center for Computational Neuroscience Berlin, Germany

    2 publications · 69 citations

    2

    h-index

1–5 of 163

Venues

  1. NeuroImage

    3 publications · 102 citations

    3

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  2. 3

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  3. arXiv (Cornell University)

    Repository · 3 publications · 53 citations

    3

    h-index

  4. Journal of Marketing Research

    2 publications · 67 citations

    2

    h-index

  5. Journal of Econometrics

    2 publications · 585 citations

    2

    h-index

1–5