Semiparametric Density Estimation

Semiparametric Density Estimation is a statistical method that combines parametric and nonparametric components to model the overall structure of a probability density function with partial assumptions about its form, allowingflexible inference while retaining efficiency.

109

Publications

6.8K

Citations

199

Authors

132

Institutions

Publications per year

2017–2026

12

Authors

199

  1. CM
    Catherine Matias

    Centre National de la Recherche Scientifique, France

    4 publications · 124 citations

    4

    h-index

  2. JQ
    Jing Qin

    National Institute of Allergy and Infectious Diseases, United States

    3 publications · 234 citations

    3

    h-index

  3. MC
    M. C. Jones

    The Open University, United Kingdom

    3 publications · 349 citations

    3

    h-index

  4. NL
    Nils Lid Hjort

    University of Oslo, Norway

    3 publications · 600 citations

    3

    h-index

  5. WK
    Whitney K. Newey

    Massachusetts Institute of Technology, United States

    3 publications · 129 citations

    3

    h-index

1–5 of 199

Venues

  1. The Annals of Statistics

    10 publications · 1K citations

    10

    h-index

  2. Journal of the American Statistical Association

    8 publications · 1.5K citations

    8

    h-index

  3. SSRN Electronic Journal

    Repository · 6 publications · 186 citations

    6

    h-index

  4. Econometric Theory

    5 publications · 776 citations

    5

    h-index

  5. Journal of nonparametric statistics

    4 publications · 106 citations

    4

    h-index

1–5