Bayesian Quantile Structural Equation Models. Issue 2 (3rd March 2016)

• Record Type: Journal Article
• Authors: Wang, Yifan; Feng, Xiang-Nan; Song, Xin-Yuan
• Is Part Of: Structural equation modeling. Volume 23:Issue 2(2016)
• Page Start: 246
• Page End: 258
• Publication Date: 2016-03-03
• Subjects: asymmetric Laplace distribution -- latent variable models -- quantile regression -- MCMC methods; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2015.1033057
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Structural equation modeling is a common multivariate technique for the assessment of the interrelationships among latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. Basic structural equation models consist of a measurement equation for characterizing latent variables through multiple observed variables and a mean regression-type structural equation for investigating how explanatory latent variables influence outcomes of interest. However, the conventional structural equation does not provide a comprehensive analysis of the relationship between latent variables. In this article, we introduce the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The estimation is conducted in a Bayesian framework with Markov Chain Monte Carlo algorithm. The posterior inference is performed with the help of asymmetric Laplace distribution. A simulation shows that the proposed method performs satisfactorily. An application to a study of chronic kidney disease is presented.