Kernel Smoothing

Kernel Smoothing is a nonparametric density estimation method that belongs to the class of spatial smoothing techniques, distinguished by its use of a kernel function to assign weighted contributions to nearby observations in order to estimate the underlying probability density function at a given point in space.

692

Publications

83.5K

Citations

1.3K

Authors

658

Institutions

Publications per year

2017–2026

95

Authors

1.3K

  1. MC
    M. C. Jones

    The Open University, United Kingdom

    11 publications · 1.9K citations

    11

    h-index

  2. JS
    J. S. Marron

    University of North Carolina at Chapel Hill, United States

    9 publications · 852 citations

    9

    h-index

  3. WK
    Wolfgang Karl Härdle

    Humboldt-Universität zu Berlin, Germany

    10 publications · 1.6K citations

    9

    h-index

  4. EM
    Enno Mammen

    Heidelberg University, Germany

    8 publications · 409 citations

    8

    h-index

  5. MP
    M. P. Wand

    UNSW Sydney, Australia

    8 publications · 929 citations

    8

    h-index

1–5 of 1.3K

Venues

  1. Journal of the American Statistical Association

    64 publications · 14.6K citations

    42

    h-index

  2. The Annals of Statistics

    44 publications · 8.4K citations

    36

    h-index

  3. Journal of nonparametric statistics

    29 publications · 923 citations

    17

    h-index

  4. 15

    h-index

  5. Biometrika

    16 publications · 2.1K citations

    13

    h-index

1–5