Candidate Model Set

A Candidate Model Set is a collection of plausible statistical or machine learning models, considered under a common modeling objective, that are explicitly defined and compared during the process of model selection to identify the most appropriate model based on specified criteria.

127

Publications

6.8K

Citations

410

Authors

226

Institutions

Publications per year

2017–2026

38

Authors

410

  1. GC
    Gerda Claeskens

    KU Leuven, Belgium

    3 publications · 77 citations

    3

    h-index

  2. PH
    Pat Hanrahan

    Stanford University, United States

    3 publications · 173 citations

    3

    h-index

  3. NU
    Norbert Ulbrich

    Freie Universität Berlin, Germany

    3 publications · 86 citations

    3

    h-index

  4. AC
    Ali Charkhi

    KU Leuven, Belgium

    3 publications · 77 citations

    3

    h-index

  5. PE
    P. Eykhoff

    Eindhoven University of Technology, Netherlands

    2 publications · 142 citations

    2

    h-index

1–5 of 410

Venues

  1. PLoS ONE

    4 publications · 144 citations

    4

    h-index

  2. 3

    h-index

  3. Statistica Sinica

    3 publications · 53 citations

    3

    h-index

  4. International Journal of Control

    2 publications · 142 citations

    2

    h-index

  5. Proteins Structure Function and Bioinformatics

    2 publications · 112 citations

    2

    h-index

1–5