Kernel Hazard Estimation

Kernel Hazard Estimation is a nonparametric statistical method that estimates the hazard function by smoothing event time data through kernel weighting, distinguishing itself from parametric approaches by relying on local averaging around observed failure times without assuming a specific underlying distribution.

40

Publications

1.2K

Citations

60

Authors

47

Institutions

Publications per year

2017–2026

1

Authors

60

  1. JP
    Jens Perch Nielsen

    City, University of London, United Kingdom

    11 publications · 335 citations

    10

    h-index

  2. GG
    George G. Roussas

    University of California, Davis, United States

    3 publications · 91 citations

    3

    h-index

  3. AQ
    Alejandro Quintela-del-Rı́o

    Universidade da Coruña, Spain

    3 publications · 48 citations

    3

    h-index

  4. MA
    Martin A. Tanner

    University of Rochester, United States

    2 publications · 254 citations

    2

    h-index

  5. MD
    María Dolores Martínez Miranda

    Universidad de Granada, Spain

    2 publications · 29 citations

    2

    h-index

1–5 of 60

Venues

  1. The Annals of Statistics

    5 publications · 420 citations

    5

    h-index

  2. Journal of nonparametric statistics

    4 publications · 69 citations

    4

    h-index

  3. Scandinavian Journal of Statistics

    3 publications · 119 citations

    3

    h-index

  4. Journal of Statistical Planning and Inference

    3 publications · 70 citations

    3

    h-index

  5. Scandinavian Actuarial Journal

    3 publications · 61 citations

    3

    h-index

1–5