NotesFAQContact Us
Collection
Advanced
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ911161
Record Type: Journal
Publication Date: 2011
Pages: 18
Abstractor: As Provided
Reference Count: 37
ISBN: N/A
ISSN: ISSN-1070-5511
Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models
Song, Xin-Yuan; Xia, Ye-Mao; Pan, Jun-Hao; Lee, Sik-Yum
Structural Equation Modeling: A Multidisciplinary Journal, v18 n1 p55-72 2011
Structural equation models have wide applications. One of the most important issues in analyzing structural equation models is model comparison. This article proposes a Bayesian model comparison statistic, namely the "L[subscript nu]"-measure for both semiparametric and parametric structural equation models. For illustration purposes, we consider a Bayesian semiparametric approach for estimation and model comparison in the context of structural equation models with fixed covariates. A finite dimensional Dirichlet process is used to model the crucial latent variables, and a blocked Gibbs sampler is implemented for estimation. Empirical performance of the "L[subscript nu]"-measure is evaluated through a simulation study. Results obtained indicate that the "L[subscript nu]"-measure, which additionally requires very minor computational effort, gives satisfactory performance. Moreover, the methodologies are demonstrated through an example with a real data set on kidney disease. Finally, the application of the "L[subscript nu]"-measure to Bayesian semiparametric nonlinear structural equation models is outlined. (Contains 3 tables.)
Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A