Bootstrap Methods

Bootstrap Methods is a resampling-based statistical methodology that estimates the sampling distribution and associated inferential quantities of estimators or test statistics by repeatedly drawing samples, typically with replacement, from the observed data or an estimated model instead of relying solely on analytic or first-order asymptotic approximations.

3.6K

Publications

388K

Citations

5K

Authors

1.8K

Institutions

Publications per year

2017–2026

565

Authors

5K

  1. PH
    Peter Hall

    Australian National University, Australia

    62 publications · 5.8K citations

    38

    h-index

  2. BE
    Bradley Efron

    Stanford University, United States

    40 publications · 26K citations

    36

    h-index

  3. JG
    James G. MacKinnon

    Queen's University, Canada

    27 publications · 11.6K citations

    24

    h-index

  4. DN
    Dimitris N. Politis

    University of California San Diego, United States

    31 publications · 3.4K citations

    24

    h-index

  5. RD
    Russell Davidson

    Queen's University, Canada

    25 publications · 10.4K citations

    21

    h-index

1–5 of 5K

Venues

  1. Journal of the American Statistical Association

    198 publications · 41.4K citations

    76

    h-index

  2. The Annals of Statistics

    183 publications · 44.2K citations

    74

    h-index

  3. Journal of Econometrics

    74 publications · 5.9K citations

    39

    h-index

  4. Biometrika

    71 publications · 5.9K citations

    35

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 80 publications · 4.4K citations

    33

    h-index

1–5