A Review of Generalized Linear Latent Variable Models and Related Computational Approaches
Pekka Korhonen,
Klaus Nordhausen,
Sara Taskinen
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 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 … Show more
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