Optimal Kernel Choice

Optimal Kernel Choice is a method in nonparametric statistics that identifies the kernel function minimizing the asymptotic mean integrated squared error in estimating a probability density function via kernel density estimation.

206

Publications

13.6K

Citations

560

Authors

292

Institutions

Publications per year

2017–2026

67

Authors

560

  1. RG
    Richard G. Baraniuk

    Rice University, United States

    4 publications · 453 citations

    4

    h-index

  2. DL
    Douglas L. Jones

    University of Illinois Urbana-Champaign, United States

    4 publications · 453 citations

    4

    h-index

  3. TG
    Théo Gasser

    University of Zurich, Switzerland

    4 publications · 800 citations

    4

    h-index

  4. MP
    Massimiliano Pontil

    University College London, United Kingdom

    3 publications · 1K citations

    3

    h-index

  5. MB
    Michael Brockmann

    Kliniken der Stadt Köln, Germany

    3 publications · 326 citations

    3

    h-index

1–5 of 560

Venues

  1. The Annals of Statistics

    8 publications · 1.1K citations

    8

    h-index

  2. Statistics & Probability Letters

    7 publications · 194 citations

    7

    h-index

  3. IEEE Transactions on Signal Processing

    5 publications · 606 citations

    5

    h-index

  4. arXiv (Cornell University)

    Repository · 5 publications · 78 citations

    5

    h-index

  5. Pattern Recognition

    4 publications · 290 citations

    4

    h-index

1–5