期刊
STATISTICS IN MEDICINE
卷 29, 期 18, 页码 1861-1874出版社
WILEY
DOI: 10.1002/sim.3915
关键词
mixture structural equation model; non-ignorable missing responses and covariates, MCMC method; model selection, modified DIC
类别
资金
- Sun Yat-Sen University [34000-3171920]
- HKSAR [GRF 450508]
- Health, Welfare and Food Bureau of HKSAR [RFCID 07060312]
In behavioral, biomedical, and social-psychological sciences, it is common to encounter latent variables and heterogeneous data. Mixture structural equation models (SEMs) are very useful methods to analyze these kinds of data. Moreover, the presence of missing data, including both missing responses and missing covariates, is an important issue in practical research. However, limited work has been done on the analysis of mixture SEMs with non-ignorable missing responses and covariates. The main objective of this paper is to develop a Bayesian approach for analyzing mixture SEMs with an unknown number of components, in which a multinomial logit model is introduced to assess the influence of some covariates on the component probability. Results of our simulation study show that the Bayesian estimates obtained by the proposed method are accurate, and the model selection procedure via a modified DIC is useful in identifying the correct number of components and in selecting an appropriate missing mechanism in the proposed mixture SEMs. A real data set related to a longitudinal study of polydrug use is employed to illustrate the methodology. Copyright (C) 2010 John Wiley & Sons, Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据