<journal article>
HYPER-PARAMETER SELECTION IN BAYESIAN STRUCTURAL EQUATION MODELS

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Abstract In the structural equation models, the maximum likelihood estimates of error variances can often turn out to be zero or negative. In order to overcome this problem, we take a Bayesian approach by spec...ifying a prior distribution for variances of error variables. Crucial issues in this modeling procedure include the selection of hyper-parameters in the prior distribution. Choosing these parameters can be viewed as a model selection and evaluation problem. We derive a model selection criterion for evaluating a Bayesian structural equation model. Monte Carlo simulations are conducted to investigate the effectiveness of the proposed modeling procedure. A real data example is also given to illustrate our procedure.show more

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Created Date 2013.02.07
Modified Date 2020.11.17

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