Balanced Bootstrap

Balanced Bootstrap is a resampling technique that belongs to the class of bootstrap methods, distinguished by its systematic replication of each observation an equal number of times across all bootstrap samples to ensure balanced representation and reduce Monte Carlo variance.

19

Publications

1.1K

Citations

35

Authors

25

Institutions

Publications per year

2017–2026

3

Authors

35

  1. PH
    Peter Hall

    Australian National University, Australia

    2 publications · 36 citations

    2

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  2. JS
    Jun Shao

    University of Wisconsin–Madison, United States

    2 publications · 109 citations

    2

    h-index

  3. JR
    John R. Gleason

    Syracuse University, United States

    2 publications · 95 citations

    2

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  4. RB
    Rudolf Beran

    University of California, Berkeley, United States

    2 publications · 121 citations

    2

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  5. BW
    Byron Wallace

    Northeastern University, United States

    2 publications · 258 citations

    2

    h-index

1–5 of 35

Venues

  1. 2

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  2. The American Statistician

    2 publications · 95 citations

    2

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  3. Statistics

    1 publications · 59 citations

    1

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  4. 1

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  5. Psychological Methods

    1 publications · 90 citations

    1

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1–5