Generalized Linear Mixed-effects Models

Generalized Linear Mixed-Effects Models is a statistical modeling framework that extends generalized linear models by incorporating both fixed and random effects to account for variability across hierarchical or correlated data structures.

2.2K

Publications

312.7K

Citations

9.8K

Authors

2.8K

Institutions

Publications per year

2017–2026

937

Variants

Generalized Linear Mixed Effects Models

Authors

9.8K

  1. GM
    Geert Molenberghs

    Hasselt University, Belgium

    25 publications · 1.6K citations

    17

    h-index

  2. GV
    Geert Verbeke

    KU Leuven, Belgium

    15 publications · 1.6K citations

    14

    h-index

  3. SR
    Sophia Rabe‐Hesketh

    University of California, Berkeley, United States

    11 publications · 1.7K citations

    11

    h-index

  4. DH
    Donald Hedeker

    University of Illinois Chicago, United States

    10 publications · 1.7K citations

    10

    h-index

  5. XL
    Xihong Lin

    Harvard University, United States

    10 publications · 1.5K citations

    10

    h-index

1–5 of 9.8K

Venues

  1. Journal of the American Statistical Association

    68 publications · 21.8K citations

    47

    h-index

  2. Biometrics

    66 publications · 11.6K citations

    39

    h-index

  3. PLoS ONE

    75 publications · 2.8K citations

    32

    h-index

  4. Statistics in Medicine

    51 publications · 3.4K citations

    27

    h-index

  5. Biometrika

    25 publications · 5.1K citations

    22

    h-index

1–5