Likelihood Computation

Likelihood Computation is a statistical procedure that belongs to the class of methods used for evaluating the probability of observed data under a specified parametric model, distinguished by its focus on calculating the likelihood function as a function of model parameters given fixed observed data.

402

Publications

40.5K

Citations

717

Authors

398

Institutions

Publications per year

2017–2026

32

Authors

717

  1. GJ
    Geoffrey J. McLachlan

    The University of Queensland, Australia

    4 publications · 212 citations

    4

    h-index

  2. SP
    Suvra Pal

    The University of Texas at Arlington, United States

    4 publications · 111 citations

    4

    h-index

  3. NB
    N. Balakrishnan

    McMaster University, Canada

    3 publications · 93 citations

    3

    h-index

  4. DG
    Dan Geiger

    Technion – Israel Institute of Technology, Israel

    3 publications · 145 citations

    3

    h-index

  5. CF
    Craig F. Ansley

    University of Chicago, United States

    3 publications · 120 citations

    3

    h-index

1–5 of 717

Venues

  1. Journal of the American Statistical Association

    19 publications · 1.3K citations

    17

    h-index

  2. Biometrika

    19 publications · 1.4K citations

    16

    h-index

  3. Journal of Econometrics

    15 publications · 1.1K citations

    13

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 13 publications · 511 citations

    12

    h-index

  5. Journal of Statistical Computation and Simulation

    JSCS · 13 publications · 473 citations

    11

    h-index

1–5