Loess Smoothing

Loess Smoothing is a nonparametric regression method that fits localized polynomial models to data points using weighted least squares, where the weights are determined by a kernel function based on the proximity of neighboring observations.

23

Publications

8K

Citations

64

Authors

38

Institutions

Publications per year

2017–2026

4

Variants

Locally Estimated Scatterplot SmoothingLocally Weighted Scatterplot SmoothingLoess

Authors

64

  1. SJ
    Susan J. Devlin

    Core Competence, United States

    2 publications · 6.3K citations

    2

    h-index

  2. WS
    William S. Cleveland

    Purdue University West Lafayette, United States

    2 publications · 6.3K citations

    2

    h-index

  3. NT
    Nicholas Tate

    University of Leicester, United Kingdom

    1 publications · 15 citations

    1

    h-index

  4. RA
    Robert Alexander

    Takeda (United States), United States

    1 publications · 21 citations

    1

    h-index

  5. TB
    Thomas Bartz–Beielstein

    TH Köln - University of Applied Sciences, Germany

    1 publications · 86 citations

    1

    h-index

1–5 of 64

Venues

  1. Journal of the American Statistical Association

    2 publications · 6.3K citations

    2

    h-index

  2. Journal of Forest Science

    1 publications · 23 citations

    1

    h-index

  3. Geophysical Journal International

    GJI · 1 publications · 29 citations

    1

    h-index

  4. Journal of Atmospheric and Oceanic Technology

    1 publications · 516 citations

    1

    h-index

  5. Statistics in Medicine

    1 publications · 357 citations

    1

    h-index

1–5