A Bayesian analysis of mixture structural equation models with non-ignorable missing responses and covariates
A Bayesian analysis of mixture structural equation models with non-ignorable missing responses and covariates
复制标题
DOI：
10.1002/sim.3915
复制
发表时间：
2010-08-15
影响因子：
2
通讯作者：
Hser, Yih-Ing
中科院分区：
文献类型：
--
作者：
Cai, Jing-Heng;Song, Xin-Yuan;Hser, Yih-Ing
在行为、生物医学和社会心理科学中，经常会遇到潜在变量和异质数据。混合结构方程模型（SEMs）是分析这类数据非常有用的方法。此外，缺失数据的存在，包括缺失的响应和缺失的协变量，是实际研究中的一个重要问题。然而，有限的工作已经做了分析的混合SEM与不可分割的缺失响应和协变量。本文的主要目的是发展一种贝叶斯方法来分析未知数量的成分的混合SEM，其中一个多项logit模型被引入到评估的一些协变量对成分概率的影响。我们的模拟研究结果表明，所提出的方法得到的贝叶斯估计是准确的，和模型选择程序，通过修改DIC是有用的，在确定正确的组件数量，并在建议的混合SEMs中选择适当的缺失机制。一个真实的数据集相关的多药使用的纵向研究来说明的方法。版权所有（C）2010约翰威利父子有限公司
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.