Robust Location Estimation

Robust Location Estimation is a statistical method that belongs to the class of location estimation techniques, distinguished by its ability to provide consistent and reliable estimates of central tendency under deviations from standard distributional assumptions, particularly in the presence of outliers or heavy-tailed noise.

360

Publications

32.2K

Citations

679

Authors

345

Institutions

Publications per year

2017–2026

48

Authors

679

  1. DM
    David M. Rocke

    University of California, Davis, United States

    8 publications · 548 citations

    8

    h-index

  2. RH
    Ruben H. Zamar

    University of British Columbia, Canada

    7 publications · 709 citations

    7

    h-index

  3. PJ
    Peter J. Bickel

    University of California, Berkeley, United States

    6 publications · 1.3K citations

    6

    h-index

  4. SV
    Stefan Van Aelst

    Ghent University, Belgium

    6 publications · 298 citations

    6

    h-index

  5. VJ
    Vı́ctor J. Yohai

    Universidad de Buenos Aires, Argentina

    6 publications · 541 citations

    6

    h-index

1–5 of 679

Venues

  1. Journal of the American Statistical Association

    43 publications · 6.3K citations

    28

    h-index

  2. The Annals of Statistics

    35 publications · 3.3K citations

    24

    h-index

  3. Technometrics

    12 publications · 1.1K citations

    12

    h-index

  4. The Annals of Mathematical Statistics

    10 publications · 8.8K citations

    10

    h-index

  5. IEEE Transactions on Vehicular Technology

    10 publications · 506 citations

    10

    h-index

1–5