Bootstrap Hypothesis Tests

Bootstrap Hypothesis Tests is a statistical hypothesis testing method that uses resampling with replacement from the observed data to estimate the sampling distribution of a test statistic under the null hypothesis, thereby enabling inference without reliance on parametric assumptions.

412

Publications

47.6K

Citations

725

Authors

425

Institutions

Publications per year

2017–2026

46

Variants

Bootstrap Hypothesis Testing

Authors

725

  1. RD
    Russell Davidson

    Queen's University, Canada

    15 publications · 9.7K citations

    15

    h-index

  2. JG
    James G. MacKinnon

    Queen's University, Canada

    14 publications · 10.6K citations

    14

    h-index

  3. DD
    Dennis D. Boos

    North Carolina State University, United States

    8 publications · 472 citations

    8

    h-index

  4. RB
    Rudolf Beran

    University of California, Berkeley, United States

    6 publications · 441 citations

    6

    h-index

  5. JL
    Joël L. Horowitz

    University of Iowa, United States

    6 publications · 631 citations

    6

    h-index

1–5 of 725

Venues

  1. Journal of the American Statistical Association

    32 publications · 10.4K citations

    23

    h-index

  2. Journal of Econometrics

    15 publications · 1.7K citations

    14

    h-index

  3. 13

    h-index

  4. SSRN Electronic Journal

    Repository · 13 publications · 495 citations

    12

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 14 publications · 748 citations

    12

    h-index

1–5