Bootstrap For Small Samples

Bootstrap for Small Samples is a resampling methodology within the class of bootstrap techniques that specifically addresses the challenges of statistical inference in small sample sizes by generating multiple synthetic samples through random sampling with replacement to improve estimation accuracy and reliability when traditional asymptotic methods are inadequate.

215

Publications

18.4K

Citations

383

Authors

259

Institutions

Publications per year

2017–2026

26

Authors

383

  1. JG
    James G. MacKinnon

    Queen's University, Canada

    7 publications · 1.2K citations

    7

    h-index

  2. MD
    Matthew D. Webb

    Carleton University, Canada

    5 publications · 830 citations

    5

    h-index

  3. RR
    Rand R. Wilcox

    University of Southern California, United States

    4 publications · 164 citations

    4

    h-index

  4. RD
    Russell Davidson

    Queen's University, Canada

    4 publications · 1K citations

    4

    h-index

  5. PH
    Peter Hall

    Australian National University, Australia

    3 publications · 720 citations

    3

    h-index

1–5 of 383

Venues

  1. 11

    h-index

  2. Journal of the American Statistical Association

    10 publications · 671 citations

    10

    h-index

  3. The Annals of Statistics

    7 publications · 571 citations

    7

    h-index

  4. Canadian Journal of Statistics

    CJS · 5 publications · 190 citations

    5

    h-index

  5. Statistics in Medicine

    5 publications · 181 citations

    5

    h-index

1–5