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A Robust Bayesian Approach for Structural Equation Models with Missing Data

Published online by Cambridge University Press:  01 January 2025

Sik-Yum Lee  and
Ye-Mao Xia
Sik-Yum Lee*
Affiliation:
The Chinese University of Hong Kong
Ye-Mao Xia
Affiliation:
The Chinese University of Hong Kong
*
Requests for reprints should be sent to Sik-Yum Lee, Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. E-mail: sylee@sta.cuhk.edu.hk
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Abstract

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear structural equation model with fixed covariates, robust Bayesian methods are developed for estimation and model comparison. Results from simulation studies are reported to reveal the characteristics of estimation. The methods are illustrated by using a real data set obtained from diabetes patients.

Keywords

robust Bayesian methodsnormal/independent distributionsnonlinear structural equation models with covariatesmodel comparison
Type
Theory and Methods
Information
Psychometrika , Volume 73 , Issue 3 , September 2008 , pp. 343 - 364
Copyright
Copyright © 2008 The Psychometric Society

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