Nearest-neighbor Density Estimation

Nearest-Neighbor Density Estimation is a nonparametric density estimation method that estimates the probability density at a given point by adapting the volume of a region centered on the point such that it contains a fixed number of nearest neighbors, thereby distinguishing it from kernel-based or fixed-bandwidth approaches.

89

Publications

10.5K

Citations

202

Authors

133

Institutions

Publications per year

2017–2026

14

Variants

K-nearest Neighbor Density EstimationK-nn Density Estimation

Authors

202

  1. KS
    Kumar Sricharan

    University of Michigan, United States

    4 publications · 154 citations

    4

    h-index

  2. YP
    Y. P. Mack

    University of California, Davis, United States

    4 publications · 535 citations

    4

    h-index

  3. AO
    Alfred O. Hero

    University of Michigan, United States

    4 publications · 154 citations

    4

    h-index

  4. WG
    Weihao Gao

    University of Illinois Urbana-Champaign, United States

    4 publications · 87 citations

    4

    h-index

  5. KF
    Keinosuke Fukunaga

    Purdue University West Lafayette, United States

    3 publications · 3.3K citations

    3

    h-index

1–5 of 202

Venues

  1. The Annals of Statistics

    7 publications · 603 citations

    7

    h-index

  2. IEEE Transactions on Information Theory

    7 publications · 3.8K citations

    7

    h-index

  3. arXiv (Cornell University)

    Repository · 6 publications · 131 citations

    6

    h-index

  4. The Astrophysical Journal

    ApJ · 3 publications · 75 citations

    3

    h-index

  5. Journal of Multivariate Analysis

    3 publications · 357 citations

    3

    h-index

1–5