Bootstrap For High-dimensional Data

Bootstrap for High-Dimensional Data is a statistical methodology within the class of bootstrap resampling techniques that specifically addresses the challenges of inference and uncertainty quantification in settings where the number of variables or parameters is comparable to or exceeds the sample size, by adapting resampling strategies to account for high-dimensional asymptotics and structural constraints.

52

Publications

3.3K

Citations

93

Authors

70

Institutions

Publications per year

2017–2026

25

Authors

93

  1. KK
    Kengo Kato

    The University of Tokyo, Japan

    5 publications · 165 citations

    6

    h-index

  2. DC
    Denis Chetverikov

    University of California, Los Angeles, United States

    5 publications · 188 citations

    5

    h-index

  3. VC
    Victor Chernozhukov

    Massachusetts Institute of Technology, United States

    5 publications · 161 citations

    5

    h-index

  4. NE
    Noureddine El Karoui

    University of California, Berkeley, United States

    3 publications · 61 citations

    3

    h-index

  5. EP
    Elizabeth Purdom

    University of California, Berkeley, United States

    3 publications · 61 citations

    3

    h-index

1–5 of 93

Venues

  1. The Annals of Statistics

    10 publications · 1.3K citations

    10

    h-index

  2. arXiv (Cornell University)

    Repository · 8 publications · 143 citations

    8

    h-index

  3. 5

    h-index

  4. 3

    h-index

  5. Scientific Reports

    2 publications · 37 citations

    2

    h-index

1–5