Approximately Unbiased Test

Approximately Unbiased Test is a statistical hypothesis testing method used in phylogenetic tree topology comparison that corrects for bias in bootstrap probability estimation by employing a multiscale bootstrap technique to produce more accurate p-values.

181

Publications

43.7K

Citations

374

Authors

204

Institutions

Publications per year

2017–2026

16

Variants

Au Test

Authors

374

  1. JT
    J. T. Gene Hwang

    Cornell University, United States

    3 publications · 111 citations

    3

    h-index

  2. HS
    Hidetoshi Shimodaira

    Tokyo Institute of Technology, Japan

    3 publications · 7.5K citations

    3

    h-index

  3. DJ
    David J. Cooke

    Glasgow Caledonian University, United Kingdom

    2 publications · 312 citations

    2

    h-index

  4. JG
    Janet G. Findlay

    Western Infirmary, United Kingdom

    2 publications · 139 citations

    2

    h-index

  5. IP
    Irwin Pollack

    University of Michigan, United States

    2 publications · 23 citations

    2

    h-index

1–5 of 374

Venues

  1. The American Statistician

    8 publications · 302 citations

    8

    h-index

  2. 8

    h-index

  3. Biometrika

    6 publications · 201 citations

    6

    h-index

  4. The Annals of Statistics

    6 publications · 567 citations

    6

    h-index

  5. The Annals of Mathematical Statistics

    6 publications · 278 citations

    6

    h-index

1–5