Optimizing Ridge Generalized Least Squares for Structural Equation Modeling. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Yang, Miao; Yuan, Ke-Hai
• Is Part Of: Structural equation modeling. Volume 26:Issue 1(2019)
• Page Start: 24
• Page End: 38
• Publication Date: 2019-01-02
• Subjects: efficiency -- empirical modeling -- Monte Carlo simulation -- parameter estimation; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1479853
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Ridge generalized least squares (RGLS) is a recently proposed estimation procedure for structural equation modeling. In the formulation of RGLS, there is a key element, ridge tuning parameter, whose value determines the efficiency of parameter estimates. This article aims to optimize RGLS by developing formulas for the ridge tuning parameter to yield the most efficient parameter estimates in practice. For the formulas to have a wide scope of applicability, they are calibrated using empirical efficiency and via many conditions on population distribution, sample size, number of variables, and model structure. Results show that RGLS with the tuning parameter determined by the formulas can substantially improve the efficiency of parameter estimates over commonly used procedures with real data being typically nonnormally distributed.