Likelihood Principle

The Likelihood Principle is a foundational tenet in statistical inference asserting that all evidence about a parameter contained in a sample is fully captured by the likelihood function, which specifies that any two sets of data yielding proportional likelihood functions provide identical information for inference about the parameter.

190

Publications

37.6K

Citations

342

Authors

205

Institutions

Publications per year

2017–2026

18

Authors

342

  1. AB
    Allan Birnbaum

    New York University, United States

    5 publications · 778 citations

    5

    h-index

  2. FD
    Feng Ding

    Jiangnan University, China

    3 publications · 64 citations

    3

    h-index

  3. RM
    Richard M. Royall

    Johns Hopkins University, United States

    3 publications · 1K citations

    3

    h-index

  4. AE
    Alexander Etz

    University of California, Irvine, United States

    2 publications · 82 citations

    2

    h-index

  5. PW
    Peter Walley

    Universidad de Granada, Spain

    2 publications · 64 citations

    2

    h-index

1–5 of 342

Venues

  1. Journal of the American Statistical Association

    19 publications · 17K citations

    17

    h-index

  2. The American Statistician

    10 publications · 688 citations

    10

    h-index

  3. Biometrika

    5 publications · 3.3K citations

    5

    h-index

  4. The Annals of Mathematical Statistics

    4 publications · 421 citations

    4

    h-index

  5. The Annals of Statistics

    4 publications · 1.9K citations

    4

    h-index

1–5