Likelihood-based Specification

Likelihood-Based Specification is a model specification approach that belongs to the class of statistical methods in which the defining criterion is the maximization of the likelihood function, distinguished by its formal reliance on the probability of observed data given a set of parameters and assumed distributional form.

36

Publications

2.3K

Citations

87

Authors

63

Institutions

Publications per year

2017–2026

5

Authors

87

  1. JW
    Jian‐qiang Wang

    Central South University, China

    3 publications · 211 citations

    3

    h-index

  2. LE
    Lynne E. Wagenknecht

    Wake Forest University, United States

    2 publications · 47 citations

    2

    h-index

  3. KK
    Kurt K. Lohman

    Wake Forest University, United States

    2 publications · 47 citations

    2

    h-index

  4. JS
    John S. Preisser

    University of North Carolina at Chapel Hill, United States

    2 publications · 47 citations

    2

    h-index

  5. CM
    Chiara Monfardini

    University of Bologna, Italy

    2 publications · 28 citations

    2

    h-index

1–5 of 87

Venues

  1. 5

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  2. Journal of Hydrology

    2 publications · 74 citations

    2

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  3. Biostatistics

    2 publications · 449 citations

    2

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  4. Biometrical Journal

    2 publications · 38 citations

    2

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  5. Genetics

    1 publications · 40 citations

    1

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1–5