Inference on Two-Part Latent Variable Analysis Model With Multivariate Longitudinal Data. Issue 5 (3rd September 2019)

• Record Type: Journal Article
• Authors: Xia, Ye-Mao; Lu, Bin; Tang, Nian-Sheng
• Is Part Of: Structural equation modeling. Volume 26:Issue 5(2019)
• Page Start: 685
• Page End: 709
• Publication Date: 2019-09-03
• Subjects: Two-part latent variable model -- MCMC -- Gibbs sampler -- model comparison; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1561294
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Semicontinuous variable analysis is a widely appreciated statistical method in such disciplines as social science, medicines, and economics. In detecting underlying structure and representing possible interrelationships, statistical analysis using a two-part model is appropriated. In this paper, we present a general extension of two-part model to the situation where the unobserved factors are included in the two parts to interpret external variability in semicontinuous variable. Auxiliary information on these factors is manifested by continuous responses via measurement model, while the interrelationships among factors are exploited through structural equation model. Moreover, under longitudinal setting, dynamic characteristics of responses between any two occasions are represented by transition model. Procedures for model fitting, parameter estimation, model selection and prediction are developed within the Bayesian paradigm. Markov Chains Monte Carlo method is used to implement posterior analysis. Empirical results including a simulation and a real example are used to illustrate the proposed methodology.