Generalized Linear Latent Variable Models

Generalized Linear Latent Variable Models is a statistical modeling framework that extends generalized linear models by incorporating latent variables to account for unobserved heterogeneity, thereby enabling the analysis of complex multivariate data with non-normal responses through a structured combination of observed covariates and underlying latent constructs.

107

Publications

7.7K

Citations

264

Authors

158

Institutions

Publications per year

2017–2026

22

Variants

GllvmGllvms

Authors

264

  1. SR
    Sophia Rabe‐Hesketh

    University of California, Berkeley, United States

    7 publications · 387 citations

    7

    h-index

  2. AP
    Andrew Pickles

    King's College London, United Kingdom

    5 publications · 212 citations

    5

    h-index

  3. FK
    Francis K. C. Hui

    Australian National University, Australia

    5 publications · 487 citations

    5

    h-index

  4. IM
    Irini Moustaki

    London School of Economics and Political Science, United Kingdom

    4 publications · 338 citations

    4

    h-index

  5. AS
    Anders Skrondal

    Norwegian Institute of Public Health, Norway

    4 publications · 214 citations

    4

    h-index

1–5 of 264

Venues

  1. Psychometrika

    8 publications · 721 citations

    8

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 4 publications · 76 citations

    4

    h-index

  3. 3

    h-index

  4. BMC Public Health

    3 publications · 209 citations

    3

    h-index

  5. Journal of Econometrics

    3 publications · 143 citations

    3

    h-index

1–5