Likelihood Asymptotics

Likelihood Asymptotics is a theoretical framework in statistical inference that characterizes the limiting behavior of likelihood functions and associated estimators as the sample size tends to infinity, providing justification for the asymptotic normality of maximum likelihood estimates and the validity of likelihood-based hypothesis tests under regularity conditions.

708

Publications

50.3K

Citations

936

Authors

517

Institutions

Publications per year

2017–2026

38

Authors

936

  1. OE
    Ole E. Barndorff–Nielsen

    Aarhus University, Denmark

    11 publications · 1.1K citations

    11

    h-index

  2. DA
    D. A. S. Fraser

    University of Toronto, Canada

    7 publications · 200 citations

    7

    h-index

  3. LR
    L. R. Shenton

    University of Georgia, United States

    6 publications · 199 citations

    6

    h-index

  4. DV
    D. V. Hinkley

    The University of Texas at Austin, United States

    6 publications · 1.8K citations

    6

    h-index

  5. GM
    Gauss M. Cordeiro

    Universidade Federal de Pernambuco, Brazil

    6 publications · 374 citations

    6

    h-index

1–5 of 936

Venues

  1. The Annals of Statistics

    102 publications · 9K citations

    51

    h-index

  2. Biometrika

    56 publications · 5K citations

    33

    h-index

  3. Journal of the American Statistical Association

    55 publications · 3.5K citations

    30

    h-index

  4. 18

    h-index

  5. Journal of Multivariate Analysis

    23 publications · 957 citations

    18

    h-index

1–5