Minimax Risk

Minimax Risk is the infimum of the maximum expected loss over all possible estimators, serving as a decision-theoretic criterion that evaluates the worst-case performance of an estimation procedure under a given model class.

339

Publications

24.4K

Citations

478

Authors

238

Institutions

Publications per year

2017–2026

67

Variants

Minimax Loss

Authors

478

  1. DL
    David L. Donoho

    Stanford University, United States

    8 publications · 1.6K citations

    8

    h-index

  2. MJ
    Martin J. Wainwright

    University of California, Berkeley, United States

    8 publications · 1.2K citations

    8

    h-index

  3. JC
    John C. Duchi

    Stanford University, United States

    7 publications · 510 citations

    7

    h-index

  4. TC
    Tommaso Cai

    University of Pennsylvania, United States

    7 publications · 464 citations

    7

    h-index

  5. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    7 publications · 1.1K citations

    7

    h-index

1–5 of 478

Venues

  1. The Annals of Statistics

    81 publications · 7.8K citations

    42

    h-index

  2. arXiv (Cornell University)

    Repository · 33 publications · 1.2K citations

    18

    h-index

  3. IEEE Transactions on Information Theory

    19 publications · 2.3K citations

    16

    h-index

  4. Bernoulli

    14 publications · 459 citations

    13

    h-index

  5. Journal of the American Statistical Association

    12 publications · 506 citations

    12

    h-index

1–5