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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/2507

Title: Bayesian structural equations model for multilevel data with missing responses and missing covariates
Authors: Kim, S
Das, S
Chen, M-H
Warren, N
Keywords: BVAR model
BVAR forecasts
Forecast accuracy
SBVAR model
SBVAR forecasts
VAR model
VAR forecasts
Issue Date: Mar-2008
Publisher: International Society for Bayesian Analysis
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
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
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
URI: http://hdl.handle.net/10204/2507
ISSN: 1931-6690
Appears in Collections:Logistics and quantitative methods
General science, engineering & technology

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