A mixture of generalized latent variable models for mixed mode and heterogeneous data

Jing-Heng Cai, Xin-Yuan Song, Kwok-Hap Lam and Edward Hak-Sing Ip

Computational Statistics & Data Analysis, 2011, vol. 55, issue 11, 2889-2907

Abstract: In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models (GLVMs) to handle mixed types of heterogeneous data. Different link functions are specified to model data of multiple types. A Bayesian approach, together with the Markov chain Monte Carlo (MCMC) method, is used to conduct the analysis. A modified DIC is used for model selection of mixture components in the GLVMs. A simulation study shows that our proposed methodology performs satisfactorily. An application of mixture GLVM to a data set from the National Longitudinal Surveys of Youth (NLSY) is presented.

Keywords: Bayesian; approach; Generalized; latent; variable; model; Heterogeneous; data (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:11:p:2889-2907

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