Least Absolute Deviations Regression

Least Absolute Deviations Regression is a regression method that belongs to the class of robust statistical estimation techniques, differing from ordinary least squares by minimizing the sum of absolute residuals rather than squared residuals, thereby reducing sensitivity to outliers.

159

Publications

11.8K

Citations

303

Authors

190

Institutions

Publications per year

2017–2026

20

Variants

L1 Norm RegressionL1 RegressionLad Regression

Authors

303

  1. PW
    Paul W. Mielke

    Colorado State University, United States

    5 publications · 139 citations

    5

    h-index

  2. KJ
    Kenneth J. Berry

    Colorado State University, United States

    5 publications · 139 citations

    5

    h-index

  3. KS
    Kristian Sabo

    University of Osijek, Croatia

    3 publications · 51 citations

    3

    h-index

  4. VC
    Victor Chernozhukov

    Massachusetts Institute of Technology, United States

    3 publications · 436 citations

    3

    h-index

  5. SY
    Seung‐Hoon Yoo

    Seoul National University of Science and Technology, South Korea

    3 publications · 71 citations

    3

    h-index

1–5 of 303

Venues

  1. Journal of the American Statistical Association

    8 publications · 1.1K citations

    8

    h-index

  2. Econometric Theory

    7 publications · 993 citations

    7

    h-index

  3. arXiv (Cornell University)

    Repository · 6 publications · 302 citations

    6

    h-index

  4. SSRN Electronic Journal

    Repository · 5 publications · 124 citations

    5

    h-index

  5. Statistics & Probability Letters

    4 publications · 166 citations

    4

    h-index

1–5