A Robust Bayesian Approach for Structural Equation Models with Missing Data

Autor/inn/en	Lee, Sik-Yum; Xia, Ye-Mao
Titel	A Robust Bayesian Approach for Structural Equation Models with Missing Data
Quelle	In: Psychometrika, 73 (2008) 3, S.343-364 (22 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-008-9060-5
Schlagwörter	Structural Equation Models; Bayesian Statistics; Evaluation Methods; Evaluation Research; Computation; Comparative Analysis; Simulation; Diseases; Patients + Suchen Sie Ihr Suchwort? Evaluationsforschung; Simulation program; Simulationsprogramm; Disease; Krankheit; Patient
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10