Resampling-based Multiple Testing

Resampling-Based Multiple Testing is a statistical methodology that belongs to the class of multiple testing correction procedures and specifically employs resampling techniques to estimate the null distribution of test statistics, thereby improving the accuracy of adjusted p-values and error rate control under dependence structures among tests.

39

Publications

7.2K

Citations

80

Authors

41

Institutions

Publications per year

2017–2026

0

Authors

80

  1. MJ
    Mark J. van der Laan

    University of California, Berkeley, United States

    9 publications · 345 citations

    9

    h-index

  2. PH
    Peter H. Westfall

    Texas Tech University, United States

    7 publications · 3.6K citations

    7

    h-index

  3. KS
    Katherine S. Pollard

    Gladstone Institutes, United States

    5 publications · 232 citations

    5

    h-index

  4. SD
    Sandrine Dudoit

    University of California, Berkeley, United States

    4 publications · 607 citations

    4

    h-index

  5. AH
    Alan Hubbard

    University of California, Berkeley, United States

    3 publications · 84 citations

    3

    h-index

1–5 of 80

Venues

  1. Journal of Statistical Planning and Inference

    4 publications · 939 citations

    4

    h-index

  2. Statistical Applications in Genetics and Molecular Biology

    SAGMB · 3 publications · 102 citations

    3

    h-index

  3. Collection of Biostatistics Research Archive

    Repository · 3 publications · 127 citations

    3

    h-index

  4. Journal of Biopharmaceutical Statistics

    JBS · 2 publications · 95 citations

    2

    h-index

  5. Biometrics

    2 publications · 1.8K citations

    2

    h-index

1–5