title: Bayesian analysis for finite mixture in non-recursive non-linear structural equation models.
abstract: This paper considers finite mixtures of structural equation models with non-linear effects of exogenous latent variables and non-recursive relations among endogenous latent variables. A Bayesian approach is developed to analyse this kind of model. In order to cope with the label switching problem, the permutation sampler is used to choose an appropriate identification constraint. Furthermore, a hybrid Markov chain Monte Carlo method that combines the Gibbs sampler, Metropolis-Hastings algorithm, and Langevin-Hastings algorithm is implemented to produce the Bayesian outputs. Finally, the proposed approach is illustrated by a simulation study and a real example.
authors.affiliation: Business School, Sun Yat-Sen University, Guangzhou, People's Republic of China. s04085590@yahoo.cn
authors.forename: Yong
authors.initials: Y
authors.lastname: Li
authors.forename: Hai-Zhong
authors.initials: HZ
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003625:Data Collection / Q000706:statistics & numerical data; D006801:Humans; D007588:Job Satisfaction; D008390:Markov Chains; D008432:Mathematical Computing; D008960:Models, Psychological; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D011594:Psychometrics / Q000706:statistics & numerical data*; D011788:Quality of Life / Q000523:psychology; D012044:Regression Analysis; D012069:Religion and Psychology; D012942:Social Sciences / Q000706:statistics & numerical data; D006113:United Kingdom; D014838:Volunteers / Q000523:psychology / Q000706:statistics & numerical data