A2 Review article, Literature review, Systematic review
A Review of Generalized Linear Latent Variable Models and Related Computational Approaches (2024)
Korhonen, P., Nordhausen, K., & Taskinen, S. (2024). A Review of Generalized Linear Latent Variable Models and Related Computational Approaches. WIREs Computational Statistics, 16(6), Article e70005. https://doi.org/10.1002/wics.70005
JYU authors or editors
Publication details
All authors or editors: Korhonen, Pekka; Nordhausen, Klaus; Taskinen, Sara
Journal or series: WIREs Computational Statistics
ISSN: 1939-5108
eISSN: 1939-0068
Publication year: 2024
Publication date: 11/11/2024
Volume: 16
Issue number: 6
Article number: e70005
Publisher: Wiley
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1002/wics.70005
Publication open access: Openly available
Publication channel open access: Partially open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/98464
Abstract
Generalized linear latent variable models (GLLVMs) have become mainstream models in this analysis of correlated, m-dimensional data. GLLVMs can be seen as a reduced-rank version of generalized linear mixed models (GLMMs) as the latent variables which are of dimension p ≪ m induce a reduced-rank covariance structure for the model. Models are flexible and can be used for various purposes, including exploratory analysis, that is, ordination analysis, estimating patterns of residual correlation, multivariate inference about measured predictors, and prediction. Recent advances in computational tools allow the development of efficient, scalable algorithms for fitting GLLMVs for any response distribution. In this article, we discuss the basics of GLLVMs and review some options for model fitting. We focus on methods that are based on likelihood inference. The implementations available in R are compared via simulation studies and an example illustrates how GLLVMs can be applied as an exploratory tool in the analysis of data from community ecology.
Keywords: factor analysis; probability calculation
Free keywords: factor analysis; Gauss–Hermite; Laplace approximation; likelihood function; MCMC; quasi-likelihood
Contributing organizations
Ministry reporting: Yes
Preliminary JUFO rating: 1
