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dc.contributor.author Kim, S
dc.contributor.author Das, Sonali
dc.contributor.author Chen, M-H
dc.contributor.author Warren, N
dc.date.accessioned 2008-11-05T05:39:11Z
dc.date.available 2008-11-05T05:39:11Z
dc.date.issued 2008-03
dc.identifier.citation Das, 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 197-224 en
dc.identifier.issn 1931-6690
dc.identifier.uri http://hdl.handle.net/10204/2507
dc.identifier.uri http://ba.stat.cmu.edu/journal/2008/vol03/issue01/chen.pdf en
dc.description Copyright: 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 197-224 en
dc.description.abstract 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 en
dc.language.iso en en
dc.publisher International Society for Bayesian Analysis en
dc.subject BVAR model en
dc.subject BVAR forecasts en
dc.subject Forecast accuracy en
dc.subject SBVAR model en
dc.subject SBVAR forecasts en
dc.subject VAR model en
dc.subject VAR forecasts en
dc.title Bayesian structural equations model for multilevel data with missing responses and missing covariates en
dc.type Article en


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