HYPER-PARAMETER SELECTION IN BAYESIAN STRUCTURAL EQUATION MODELS | Zendy

Kei Hirose | Zendy; Shuichi Kawano | Zendy; Daisuke Miike | Zendy; Sadanori Konishi | Zendy

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HYPER-PARAMETER SELECTION IN BAYESIAN STRUCTURAL EQUATION MODELS

Author(s) -

Kei Hirose,

Shuichi Kawano,

Daisuke Miike,

Sadanori Konishi

Publication year - 2010

Publication title -

bulletin of informatics and cybernetics

Language(s) - English

Resource type - Journals

eISSN - 2435-743X

pISSN - 0286-522X

DOI - 10.5109/25906

Subject(s) - bayesian probability , selection (genetic algorithm) , structural equation modeling , mathematics , statistics , model selection , bayes factor , computational biology , computer science , econometrics , bayesian inference , biology , artificial intelligence

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 prob- lem, we take a Bayesian approach by specifying 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 sim- ulations are conducted to investigate the effectiveness of our proposed modeling procedure. A real data example is also given to illustrate our procedure.

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