Prediction Error Decomposition

Prediction Error Decomposition is a theoretical framework in statistical learning that decomposes the expected prediction error of a model into constituent components attributable to bias, variance, and irreducible noise, thereby elucidating the tradeoffs governing model performance.

69

Publications

6.1K

Citations

205

Authors

117

Institutions

Publications per year

2017–2026

35

Authors

205

  1. OB
    Othman Bouabdallah

    Banque de France, France

    2 publications · 35 citations

    2

    h-index

  2. PM
    Paul M.J. Van den Hof

    Delft University of Technology, Netherlands

    2 publications · 224 citations

    2

    h-index

  3. DZ
    Ding‐Xuan Zhou

    City University of Hong Kong, Hong Kong SAR China

    2 publications · 93 citations

    2

    h-index

  4. SJ
    Steven J. Sherman

    Indiana University Bloomington, United States

    2 publications · 553 citations

    2

    h-index

  5. XB
    Xavier Bombois

    Delft University of Technology, Netherlands

    2 publications · 224 citations

    2

    h-index

1–5 of 205

Venues

  1. 4

    h-index

  2. Journal of Hydrology

    2 publications · 54 citations

    3

    h-index

  3. Journal of Personality and Social Psychology

    2 publications · 553 citations

    2

    h-index

  4. International Journal of Control

    2 publications · 163 citations

    2

    h-index

  5. 2

    h-index

1–5