Resampling-based Hypothesis Testing

Resampling-Based Hypothesis Testing is a statistical inference method that belongs to the class of resampling techniques and is distinguished by its use of repeated sampling from observed data to estimate the sampling distribution of a test statistic under the null hypothesis, thereby enabling hypothesis testing without relying on parametric distributional assumptions.

204

Publications

28.8K

Citations

389

Authors

242

Institutions

Publications per year

2017–2026

23

Authors

389

  1. MJ
    Mark J. van der Laan

    University of California, Berkeley, United States

    8 publications · 333 citations

    8

    h-index

  2. MW
    Michael Wolf

    University of Zurich, Switzerland

    7 publications · 1.3K citations

    7

    h-index

  3. JP
    Joseph P. Romano

    Stanford University, United States

    5 publications · 220 citations

    5

    h-index

  4. PH
    Peter H. Westfall

    Texas Tech University, United States

    5 publications · 3.4K citations

    5

    h-index

  5. WL
    Wei‐Yin Loh

    University of Wisconsin–Madison, United States

    5 publications · 199 citations

    5

    h-index

1–5 of 389

Venues

  1. Journal of the American Statistical Association

    21 publications · 1.6K citations

    19

    h-index

  2. Technometrics

    9 publications · 3.8K citations

    9

    h-index

  3. Biometrics

    8 publications · 2K citations

    8

    h-index

  4. SSRN Electronic Journal

    Repository · 6 publications · 283 citations

    6

    h-index

  5. The Annals of Statistics

    6 publications · 2.2K citations

    6

    h-index

1–5