Residual Bootstrap

Residual Bootstrap is a resampling technique in statistical inference where the bootstrap samples are constructed by resampling the residuals of a fitted model and adding them back to the predicted values to generate new response variables, thereby preserving the explanatory variables' structure while accounting for model uncertainty.

74

Publications

5.3K

Citations

176

Authors

111

Institutions

Publications per year

2017–2026

12

Variants

Residual Bootstrapping

Authors

176

  1. ŚG
    Śılvia Gonçalves

    Université de Montréal, Canada

    5 publications · 861 citations

    5

    h-index

  2. LK
    Lutz Kilian

    University of Michigan, United States

    3 publications · 784 citations

    3

    h-index

  3. WG
    Wenceslao González–Manteiga

    Universidade de Santiago de Compostela, Spain

    3 publications · 361 citations

    3

    h-index

  4. BP
    Benoît Perron

    Université de Montréal, Canada

    3 publications · 144 citations

    3

    h-index

  5. EP
    Efstathios Paparoditis

    University of Cyprus, Cyprus

    3 publications · 212 citations

    3

    h-index

1–5 of 176

Venues

  1. 4

    h-index

  2. Biometrika

    3 publications · 66 citations

    3

    h-index

  3. Journal of Time Series Analysis

    3 publications · 84 citations

    3

    h-index

  4. Scandinavian Journal of Statistics

    2 publications · 62 citations

    2

    h-index

  5. Econometrica

    2 publications · 259 citations

    2

    h-index

1–5