Generating Nonnormal Multivariate Data Using Copulas: Applications to SEM

Autor/inn/en	Mair, Patrick; Satorra, Albert; Bentler, Peter M.
Titel	Generating Nonnormal Multivariate Data Using Copulas: Applications to SEM
Quelle	In: Multivariate Behavioral Research, 47 (2012) 4, S.547-565 (19 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
DOI	10.1080/00273171.2012.692629
Schlagwörter	Structural Equation Models; Data; Monte Carlo Methods; Probability; Robustness (Statistics) + Suchen Sie Ihr Suchwort? Daten; Monte-Carlo-Methode; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Widerstandsfähigkeit
Abstract	This article develops a procedure based on copulas to simulate multivariate nonnormal data that satisfy a prespecified variance-covariance matrix. The covariance matrix used can comply with a specific moment structure form (e.g., a factor analysis or a general structural equation model). Thus, the method is particularly useful for Monte Carlo evaluation of structural equation models within the context of nonnormal data. The new procedure for nonnormal data simulation is theoretically described and also implemented in the widely used R environment. The quality of the method is assessed by Monte Carlo simulations. A 1-sample test on the observed covariance matrix based on the copula methodology is proposed. This new test for evaluating the quality of a simulation is defined through a particular structural model specification and is robust against normality violations. (Contains 2 figures and 2 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10