Generalized latent variable models for location, scale, and shape parameters

Camilo Cardenas Hurtado, Irini Moustaki, Yunxiao Chen and Giampiero Marra

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We introduce a general framework for latent variable modeling, named Generalized Latent Variable Models for Location, Scale, and Shape parameters (GLVM-LSS). This framework extends the generalized linear latent variable model beyond the exponential family distributional assumption and enables the modeling of distributional parameters other than the mean (location parameter), such as scale and shape parameters, as functions of latent variables. Model parameters are estimated via maximum likelihood. We present two real-world applications on public opinion research and educational testing, and evaluate the model’s performance in terms of parameter recovery through extensive simulation studies. Our results suggest that the GLVM-LSS is a valuable tool in applications where modeling higher-order moments of the observed variables through latent variables is of substantive interest. The proposed model is implemented in the R package glvmlss, available online.

Keywords: latent variable models; distributional regression; GAMLSS; EM algorithm; heteroscedasticity (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2025-03-06
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Published in Psychometrika, 6, March, 2025. ISSN: 0033-3123

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:127387

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