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B0340
Title: Bayesian regularized multivariate generalized latent variable models Authors:  Xiangnan Feng - The Chinese University of Hong Kong (Hong Kong) [presenting]
Hao-Tian Wu - Sun Yat-Sen University (China)
Xinyuan Song - Chinese University of Hong Kong (Hong Kong)
Abstract: A multivariate generalized latent variable model is considered 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.