Kim, SDas, SonaliChen, M-HWarren, N2008-11-052008-11-052008-03Das, S, Chen, M, Kim, S and Warren, N. 2008. Bayesian structural equations model for multilevel data with missing responses and missing covariates. Bayesian Analysis, Vol. 3(1), pp 197-2241931-6690http://hdl.handle.net/10204/2507http://ba.stat.cmu.edu/journal/2008/vol03/issue01/chen.pdfCopyright: 2008 Internation Society for Bayesian Analysis. This is the author's pre print version of the work. The definitive version is published in Bayesian Analysis, Vol 3(1), pp 197-224Motivated by a large multilevel survey conducted by the US Veterans Health Administration (VHA), we propose a structural equations model which involves a set of latent variables to capture dependence between different responses, a set of facility level random effects to capture facility heterogeneity and dependence between individuals in the same facility, and a set of covariates to account for individual heterogeneity. Identifiability associated with structural equations modeling is addressed and properties of the proposed model are carefully examined. An effective 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 Carlos sampling is used to carry out Bayesian posterior computation. 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 methodologyenBVAR modelBVAR forecastsForecast accuracySBVAR modelSBVAR forecastsVAR modelVAR forecastsArticleKim, S., Das, S., Chen, M., & Warren, N. (2008). Bayesian structural equations model for multilevel data with missing responses and missing covariates. http://hdl.handle.net/10204/2507Kim, S, Sonali Das, M-H Chen, and N Warren "Bayesian structural equations model for multilevel data with missing responses and missing covariates." (2008) http://hdl.handle.net/10204/2507Kim S, Das S, Chen M, Warren N. Bayesian structural equations model for multilevel data with missing responses and missing covariates. 2008; http://hdl.handle.net/10204/2507.TY - Article AU - Kim, S AU - Das, Sonali AU - Chen, M-H AU - Warren, N AB - Motivated by a large multilevel survey conducted by the US Veterans Health Administration (VHA), we propose a structural equations model which involves a set of latent variables to capture dependence between different responses, a set of facility level random effects to capture facility heterogeneity and dependence between individuals in the same facility, and a set of covariates to account for individual heterogeneity. Identifiability associated with structural equations modeling is addressed and properties of the proposed model are carefully examined. An effective 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 Carlos sampling is used to carry out Bayesian posterior computation. 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 DA - 2008-03 DB - ResearchSpace DP - CSIR KW - BVAR model KW - BVAR forecasts KW - Forecast accuracy KW - SBVAR model KW - SBVAR forecasts KW - VAR model KW - VAR forecasts LK - https://researchspace.csir.co.za PY - 2008 SM - 1931-6690 T1 - Bayesian structural equations model for multilevel data with missing responses and missing covariates TI - Bayesian structural equations model for multilevel data with missing responses and missing covariates UR - http://hdl.handle.net/10204/2507 ER -