Isotonic Regression

Isotonic Regression is a non-parametric regression technique that estimates a monotonic function by minimizing the squared error between observed responses and fitted values subject to the constraint that the predicted outputs are non-decreasing (or non-increasing) with respect to the input variables.

246

Publications

17.3K

Citations

512

Authors

236

Institutions

Publications per year

2017–2026

59

Authors

512

  1. BS
    Bodhisattva Sen

    Columbia University, United States

    7 publications · 304 citations

    7

    h-index

  2. AG
    Adityanand Guntuboyina

    University of California, Berkeley, United States

    5 publications · 261 citations

    5

    h-index

  3. TR
    Tim Robertson

    University of Iowa, United States

    5 publications · 318 citations

    5

    h-index

  4. QF
    Quentin F. Stout

    University of Michigan, United States

    5 publications · 173 citations

    5

    h-index

  5. QH
    Qiyang Han

    Rutgers Sexual and Reproductive Health and Rights, Netherlands

    5 publications · 129 citations

    5

    h-index

1–5 of 512

Venues

  1. Statistics in Medicine

    15 publications · 876 citations

    15

    h-index

  2. The Annals of Statistics

    20 publications · 973 citations

    15

    h-index

  3. Journal of the American Statistical Association

    15 publications · 1.1K citations

    13

    h-index

  4. arXiv (Cornell University)

    Repository · 13 publications · 393 citations

    11

    h-index

  5. Biometrics

    9 publications · 840 citations

    9

    h-index

1–5