Kernel Regression

Kernel Regression is a nonparametric regression method that estimates the conditional expectation of a dependent variable by weighting nearby observed data points according to a kernel function, thereby distinguishing itself from parametric regression techniques through its reliance on local smoothing rather than global functional form assumptions.

1.8K

Publications

148.4K

Citations

4.2K

Authors

1.5K

Institutions

Publications per year

2017–2026

568

Authors

4.2K

  1. WK
    Wolfgang Karl Härdle

    Humboldt-Universität zu Berlin, Germany

    17 publications · 1.7K citations

    16

    h-index

  2. TG
    Théo Gasser

    University of Zurich, Switzerland

    16 publications · 1.9K citations

    16

    h-index

  3. PV
    Philippe Vieu

    Université Toulouse III - Paul Sabatier, France

    14 publications · 1.4K citations

    14

    h-index

  4. RJ
    Raymond J. Carroll

    Texas A&M University, United States

    14 publications · 1.6K citations

    13

    h-index

  5. HM
    Hans‐Georg Müller

    University of California, Davis, United States

    12 publications · 1.4K citations

    12

    h-index

1–5 of 4.2K

Venues

  1. The Annals of Statistics

    69 publications · 9.7K citations

    50

    h-index

  2. Journal of the American Statistical Association

    75 publications · 11.3K citations

    48

    h-index

  3. Journal of nonparametric statistics

    47 publications · 1.9K citations

    24

    h-index

  4. arXiv (Cornell University)

    Repository · 54 publications · 2.1K citations

    23

    h-index

  5. Journal of Statistical Planning and Inference

    24 publications · 848 citations

    18

    h-index

1–5