Tags

Type your tag names separated by a space and hit enter

Bayesian analysis for finite mixture in non-recursive non-linear structural equation models.
Br J Math Stat Psychol. 2010 May; 63(Pt 2):361-77.BJ

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+Show Affiliations

Business School, Sun Yat-Sen University, Guangzhou, People's Republic of China. s04085590@yahoo.cnNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

19719904

Citation

Li, Yong, and Hai-Zhong Wang. "Bayesian Analysis for Finite Mixture in Non-recursive Non-linear Structural Equation Models." The British Journal of Mathematical and Statistical Psychology, vol. 63, no. Pt 2, 2010, pp. 361-77.
Li Y, Wang HZ. Bayesian analysis for finite mixture in non-recursive non-linear structural equation models. Br J Math Stat Psychol. 2010;63(Pt 2):361-77.
Li, Y., & Wang, H. Z. (2010). Bayesian analysis for finite mixture in non-recursive non-linear structural equation models. The British Journal of Mathematical and Statistical Psychology, 63(Pt 2), 361-77. https://doi.org/10.1348/000711009X466367
Li Y, Wang HZ. Bayesian Analysis for Finite Mixture in Non-recursive Non-linear Structural Equation Models. Br J Math Stat Psychol. 2010;63(Pt 2):361-77. PubMed PMID: 19719904.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Bayesian analysis for finite mixture in non-recursive non-linear structural equation models. AU - Li,Yong, AU - Wang,Hai-Zhong, Y1 - 2009/08/28/ PY - 2009/9/2/entrez PY - 2009/9/2/pubmed PY - 2010/6/17/medline SP - 361 EP - 77 JF - The British journal of mathematical and statistical psychology JO - Br J Math Stat Psychol VL - 63 IS - Pt 2 N2 - 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. SN - 0007-1102 UR - https://www.unboundmedicine.com/medline/citation/19719904/Bayesian_analysis_for_finite_mixture_in_non_recursive_non_linear_structural_equation_models_ L2 - https://doi.org/10.1348/000711009X466367 DB - PRIME DP - Unbound Medicine ER -