High-dimensional Estimation

High-Dimensional Estimation is a statistical methodology within estimation theory that addresses the challenge of inferring unknown parameters from data in settings where the number of parameters to estimate is comparable to or larger than the sample size, thereby requiring specialized techniques to manage complexity, prevent overfitting, and ensure reliable inference.

544

Publications

49.3K

Citations

1.1K

Authors

471

Institutions

Publications per year

2017–2026

218

Authors

1.1K

  1. VC
    Victor Chernozhukov

    Massachusetts Institute of Technology, United States

    14 publications · 4K citations

    13

    h-index

  2. JF
    Jianqing Fan

    Princeton University, United States

    12 publications · 2.5K citations

    12

    h-index

  3. AB
    Alexandre Belloni

    Duke University, United States

    9 publications · 994 citations

    9

    h-index

  4. MJ
    Martin J. Wainwright

    University of California, Berkeley, United States

    9 publications · 2.4K citations

    9

    h-index

  5. CH
    Christian Hansen

    University of Chicago, United States

    8 publications · 3.6K citations

    8

    h-index

1–5 of 1.1K

Venues

  1. The Annals of Statistics

    69 publications · 9K citations

    45

    h-index

  2. Journal of the American Statistical Association

    40 publications · 3.7K citations

    28

    h-index

  3. arXiv (Cornell University)

    Repository · 61 publications · 2.2K citations

    27

    h-index

  4. Electronic Journal of Statistics

    18 publications · 1.3K citations

    14

    h-index

  5. 13

    h-index

1–5