Weighted Bootstrap

Weighted Bootstrap is a resampling technique in phylogenetics that assigns non-uniform probabilities to sites or characters during replicate dataset generation, distinguishing it from conventional bootstrap methods by incorporating differential weighting to reflect varying confidence or evolutionary signal across data positions.

47

Publications

1.9K

Citations

87

Authors

60

Institutions

Publications per year

2017–2026

8

Authors

87

  1. SM
    Shuangge Ma

    Yale University, United States

    6 publications · 424 citations

    6

    h-index

  2. SG
    Spyros G. Zarkos

    Universidade Federal de Pernambuco, Brazil

    2 publications · 71 citations

    2

    h-index

  3. PH
    Peter Hall

    Australian National University, Australia

    2 publications · 181 citations

    2

    h-index

  4. SB
    Salim Bouzebda

    Université de Technologie de Compiègne, France

    2 publications · 31 citations

    2

    h-index

  5. LH
    Lajos Horváth

    University of Utah, United States

    2 publications · 64 citations

    2

    h-index

1–5 of 87

Venues

  1. The Annals of Statistics

    4 publications · 228 citations

    4

    h-index

  2. Canadian Journal of Statistics

    CJS · 3 publications · 105 citations

    3

    h-index

  3. Biometrics

    2 publications · 140 citations

    2

    h-index

  4. Statistics & Probability Letters

    2 publications · 55 citations

    2

    h-index

  5. Electronic Journal of Statistics

    2 publications · 81 citations

    2

    h-index

1–5