Bootstrap Model Selection

Bootstrap Model Selection is a statistical model selection procedure that uses bootstrap resampling techniques to assess the stability and performance of candidate models, thereby distinguishing itself from traditional model selection methods by incorporating resampling-based uncertainty estimation in the selection process.

120

Publications

23K

Citations

271

Authors

171

Institutions

Publications per year

2017–2026

15

Authors

271

  1. WS
    Willi Sauerbrei

    University of Freiburg, Germany

    5 publications · 987 citations

    5

    h-index

  2. JU
    J.S. Urban Hjorth

    University of Iowa, United States

    4 publications · 578 citations

    4

    h-index

  3. KK
    Keith Knight

    University of Toronto, Canada

    3 publications · 207 citations

    3

    h-index

  4. BE
    Bradley Efron

    Stanford University, United States

    3 publications · 4.5K citations

    3

    h-index

  5. SJ
    Silke Janitza

    Zimmer Biomet (Germany), Germany

    3 publications · 146 citations

    3

    h-index

1–5 of 271

Venues

  1. Journal of the American Statistical Association

    9 publications · 1.5K citations

    9

    h-index

  2. Statistics in Medicine

    7 publications · 982 citations

    7

    h-index

  3. Journal of Computational and Graphical Statistics

    JCGS · 5 publications · 147 citations

    5

    h-index

  4. Biometrical Journal

    4 publications · 120 citations

    4

    h-index

  5. 3

    h-index

1–5