Bayesian Regularized Multivariate Generalized Latent Variable Models. Issue 3 (4th May 2017)

• Record Type: Journal Article
• Authors: Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan
• Is Part Of: Structural equation modeling. Volume 24:Issue 3(2017)
• Page Start: 341
• Page End: 358
• Publication Date: 2017-05-04
• Subjects: Bayesian adaptive lasso -- generalized latent variable model -- MCMC methods -- mixed-type data; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2016.1257353
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : We consider a multivariate generalized latent variable model to investigate the effects of observable and latent explanatory variables on multiple responses of interest. Various types of correlated responses, such as continuous, count, ordinal, and nominal variables, are considered in the regression. A generalized confirmatory factor analysis model that is capable of managing mixed-type data is proposed to characterize latent variables via correlated observed indicators. In addressing the complicated structure of the proposed model, we introduce continuous underlying measurements to provide a unified model framework for mixed-type data. We develop a multivariate version of the Bayesian adaptive least absolute shrinkage and selection operator procedure, which is implemented with a Markov chain Monte Carlo (MCMC) algorithm in a full Bayesian context, to simultaneously conduct estimation and model selection. The empirical performance of the proposed methodology is demonstrated through a simulation study. An application of the proposed method to a study of adolescent substance abuse based on the National Longitudinal Survey of Youth is presented.