Bootstrap Estimation

Bootstrap Estimation is a resampling technique that belongs to the family of statistical inference methods, distinguished by its approach of repeatedly sampling with replacement from the observed data to estimate the sampling distribution of a statistic, thereby enabling robust assessment of model variability and uncertainty.

2.1K

Publications

239.7K

Citations

3.2K

Authors

1.4K

Institutions

Publications per year

2017–2026

316

Authors

3.2K

  1. PH
    Peter Hall

    Australian National University, Australia

    46 publications · 3.8K citations

    32

    h-index

  2. BE
    Bradley Efron

    Stanford University, United States

    31 publications · 21.3K citations

    28

    h-index

  3. JG
    James G. MacKinnon

    Queen's University, Canada

    25 publications · 11.5K citations

    23

    h-index

  4. DN
    Dimitris N. Politis

    University of California San Diego, United States

    24 publications · 5.8K citations

    18

    h-index

  5. RD
    Russell Davidson

    Queen's University, Canada

    19 publications · 10.2K citations

    17

    h-index

1–5 of 3.2K

Venues

  1. Journal of the American Statistical Association

    131 publications · 36.3K citations

    64

    h-index

  2. The Annals of Statistics

    120 publications · 36.6K citations

    60

    h-index

  3. Journal of Econometrics

    45 publications · 3.8K citations

    29

    h-index

  4. Biometrika

    50 publications · 4.8K citations

    28

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 46 publications · 2.8K citations

    25

    h-index

1–5