Quantile Loss

Quantile Loss is a type of loss function used in regression analysis that generalizes the absolute error by asymmetrically weighting overestimation and underestimation according to a specified quantile level.

92

Publications

4.5K

Citations

228

Authors

134

Institutions

Publications per year

2017–2026

44

Variants

Check Loss

Authors

228

  1. TL
    Tae‐Hwy Lee

    University of California, Riverside, United States

    3 publications · 317 citations

    3

    h-index

  2. IS
    Ingo Steinwart

    Los Alamos National Laboratory, United States

    2 publications · 291 citations

    2

    h-index

  3. YS
    Y S Kan

    2 publications · 171 citations

    2

    h-index

  4. BS
    Burak Saltoğlu

    Marmara University, Türkiye

    2 publications · 235 citations

    2

    h-index

  5. KS
    Klaus Schaeck

    University of Bristol, United Kingdom

    2 publications · 94 citations

    2

    h-index

1–5 of 228

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 154 citations

    7

    h-index

  2. IEEE Access

    3 publications · 103 citations

    4

    h-index

  3. Journal of the Operational Research Society

    OR · 3 publications · 202 citations

    3

    h-index

  4. Automation and Remote Control

    3 publications · 36 citations

    3

    h-index

  5. Journal of Econometrics

    2 publications · 94 citations

    2

    h-index

1–5