Bayesian Analysis of Mixture Structural Equation Models With an Unknown Number of Components. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Liu, Hefei; Song, Xin Yuan
• Is Part Of: Structural equation modeling. Volume 25:Issue 1(2018)
• Page Start: 41
• Page End: 55
• Publication Date: 2018-01-02
• Subjects: Bayesian method -- latent variables -- mixture models -- RJMCMC algorithm; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1372688
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Multivariate heterogenous data with latent variables are common in many fields such as biological, medical, behavioral, and social-psychological sciences. Mixture structural equation models are multivariate techniques used to examine heterogeneous interrelationships among latent variables. In the analysis of mixture models, determination of the number of mixture components is always an important and challenging issue. This article aims to develop a full Bayesian approach with the use of reversible jump Markov chain Monte Carlo method to analyze mixture structural equation models with an unknown number of components. The proposed procedure can simultaneously and efficiently select the number of mixture components and conduct parameter estimation. Simulation studies show the satisfactory empirical performance of the method. The proposed method is applied to study risk factors of osteoporotic fractures in older people.