Adaptive Robust Estimation

Adaptive Robust Estimation is a statistical methodology that belongs to the class of robust estimation techniques, distinguished by its ability to automatically adjust its estimating functions or tuning parameters based on the data structure and contamination level to achieve optimal trade-offs between efficiency and robustness.

78

Publications

2.9K

Citations

204

Authors

131

Institutions

Publications per year

2017–2026

27

Authors

204

  1. JF
    Jianqing Fan

    Princeton University, United States

    4 publications · 352 citations

    4

    h-index

  2. DK
    Donggyu Kim

    Korea Advanced Institute of Science and Technology, South Korea

    3 publications · 47 citations

    3

    h-index

  3. MS
    Minseok Shin

    Princeton University, United States

    3 publications · 47 citations

    3

    h-index

  4. KF
    Katherine Fradette

    University of Manitoba, Canada

    2 publications · 82 citations

    2

    h-index

  5. HJ
    H. J. Keselman

    University of Manitoba, Canada

    2 publications · 82 citations

    2

    h-index

1–5 of 204

Venues

  1. 4

    h-index

  2. The Annals of Statistics

    3 publications · 360 citations

    3

    h-index

  3. 3

    h-index

  4. 2

    h-index

  5. Sensors

    2 publications · 74 citations

    2

    h-index

1–5