文章基本信息

标题：A Bayesian Structural Equations Model for Multilevel Data with Missing Responses and Missing Covariates
本地全文：下载
作者：Sonali Das ; Ming-Hui Chen ; Sungduk Kim 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2008
卷号：03
期号：01
页码：197-224
出版社：International Society for Bayesian Analysis
摘要：Motivated by a large multilevel survey conducted by the US Veterans Health Administration (VHA), we propose a structural equations model which in- volves a set of latent variables to capture dependence between di erent responses, a set of facility level random e ects to capture facility heterogeneity and dependence between individuals within the same facility, and a set of covariates to account for individual heterogeneity. Identiability associated with structural equations mod- eling is addressed and properties of the proposed model are carefully examined. An e ective and practically useful modeling strategy is developed to deal with missing responses and to model missing covariates in the structural equations framework. Markov chain Monte Carlo sampling is used to carry out Bayesian posterior com- putation. Several variations of the proposed model are considered and compared via the deviance information criterion. A detailed analysis of the VHA all employee survey data is presented to illustrate the proposed methodology.
关键词：DIC, Latent variable, Markov chain Monte Carlo, missing at random, random e ects, VHA all employee survey data