Bootstrap For Robust Statistics

Bootstrap for Robust Statistics is a statistical methodology that belongs to the class of resampling techniques and specifically adapts the bootstrap framework to estimate the sampling distribution of robust estimators in the presence of outliers or model misspecification.

23

Publications

3.1K

Citations

50

Authors

26

Institutions

Publications per year

2017–2026

3

Authors

50

  1. GW
    Gert Willems

    KU Leuven, Belgium

    2 publications · 85 citations

    2

    h-index

  2. AA
    Andreas Alfons

    KU Leuven, Belgium

    2 publications · 254 citations

    2

    h-index

  3. SV
    Stefan Van Aelst

    Ghent University, Belgium

    2 publications · 85 citations

    2

    h-index

  4. VK
    Vikas Kumar

    University of Hyderabad, India

    1 publications · 15 citations

    1

    h-index

  5. JH
    Janet Hergt

    The University of Melbourne, Australia

    1 publications · 85 citations

    1

    h-index

1–5 of 50

Venues

  1. Journal of Statistical Software

    JSS · 2 publications · 81 citations

    2

    h-index

  2. Biometrika

    2 publications · 73 citations

    2

    h-index

  3. Journal of Neurophysiology

    JN · 1 publications · 53 citations

    1

    h-index

  4. Chemical Geology

    1 publications · 85 citations

    1

    h-index

  5. IEEE Power and Energy Magazine

    1 publications · 33 citations

    1

    h-index

1–5