Bayesian model selection for mixtures of structural equation models with an unknown number of components

The British Journal of Mathematical and Statistical Psychology
Sik-Yum Lee, Xinyuan Song

Abstract

This paper considers mixtures of structural equation models with an unknown number of components. A Bayesian model selection approach is developed based on the Bayes factor. A procedure for computing the Bayes factor is developed via path sampling, which has a number of nice features. The key idea is to construct a continuous path linking the competing models; then the Bayes factor can be estimated efficiently via grids in [0, 1] and simulated observations that are generated by the Gibbs sampler from the posterior distribution. Bayesian estimates of the structural parameters, latent variables, as well as other statistics can be produced as by-products. The properties and merits of the proposed procedure are discussed and illustrated by means of a simulation study and a real example.

Citations

Nov 17, 2007·Statistics in Medicine·Sik-Yum LeeXin-Yuan Song
Jul 26, 2011·Statistics in Medicine·Xinming An, Peter M Bentler
Dec 25, 2009·The British Journal of Mathematical and Statistical Psychology·Jing-Heng Cai, Xin-Yuan Song
Dec 14, 2011·Statistics in Medicine·Zhaohua Lu, Xinyuan Song
Apr 1, 2005·Multivariate Behavioral Research·Xin-Yuan Song, Sik-Yum Lee
Jul 1, 2011·Multivariate Behavioral Research·Zhenqiu Laura LuGitta Lubke
Dec 12, 2017·Statistical Methods in Medical Research·Deng PanXinyuan Song
May 20, 2006·The British Journal of Mathematical and Statistical Psychology·Sik-Yum Lee, Nian-Sheng Tang
Jul 30, 2020·Statistical Methods in Medical Research·Hefei LiuBaoxue Zhang

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