High-dimensional Statistics

High-Dimensional Statistics is a branch of statistical science that focuses on the development and analysis of methods for inference and prediction when the number of variables or parameters approaches or exceeds the number of observations, thereby distinguishing it from classical statistics by addressing the challenges posed by large-dimensional data spaces.

1.6K

Publications

129.7K

Citations

3.1K

Authors

1.1K

Institutions

Publications per year

2017–2026

602

Authors

3.1K

  1. PB
    Peter Bühlmann

    ETH Zurich, Switzerland

    21 publications · 3.9K citations

    19

    h-index

  2. JF
    Jianqing Fan

    Princeton University, United States

    21 publications · 7.6K citations

    18

    h-index

  3. VC
    Victor Chernozhukov

    Massachusetts Institute of Technology, United States

    17 publications · 1.8K citations

    15

    h-index

  4. LW
    Larry Wasserman

    Carnegie Mellon University, United States

    14 publications · 2.2K citations

    13

    h-index

  5. HL
    Han Liu

    Princeton University, United States

    13 publications · 1.4K citations

    12

    h-index

1–5 of 3.1K

Venues

  1. The Annals of Statistics

    110 publications · 13.2K citations

    53

    h-index

  2. Journal of the American Statistical Association

    80 publications · 10.1K citations

    41

    h-index

  3. arXiv (Cornell University)

    Repository · 165 publications · 7.3K citations

    36

    h-index

  4. Biometrika

    41 publications · 2.8K citations

    26

    h-index

  5. Journal of Multivariate Analysis

    46 publications · 2.2K citations

    25

    h-index

1–5