Structural Equation Modeling With Unknown Population Distributions: Ridge Generalized Least Squares. Issue 2 (3rd March 2016)

• Record Type: Journal Article
• Authors: Yuan, Ke-Hai; Chan, Wai
• Is Part Of: Structural equation modeling. Volume 23:Issue 2(2016)
• Page Start: 163
• Page End: 179
• Publication Date: 2016-03-03
• Subjects: efficiency -- Monte Carlo -- rescaled statistics -- root mean square error -- sandwich-type covariance matrix -- type I errors; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2015.1077335
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : This article proposes 2 classes of ridge generalized least squares (GLS) procedures for structural equation modeling (SEM) with unknown population distributions. The weight matrix for the first class of ridge GLS is obtained by combining the sample fourth-order moment matrix with the identity matrix. The weight matrix for the second class is obtained by combining the sample fourth-order moment matrix with its diagonal matrix. Empirical results indicate that, with data from an unknown population distribution, parameter estimates by ridge GLS can be much more accurate than those by either GLS or normal-distribution-based maximum likelihood; and standard errors of the parameter estimates also become more accurate in predicting the empirical ones. Rescaled and adjusted statistics are proposed for overall model evaluation, and they also perform much better than the default statistic following from the GLS method. The use of the ridge GLS procedures is illustrated with a real data set.