Original paper
A Regularized GLS for Structural Equation Modeling
Volume: 24, Issue: 5, Pages: 657 - 665
Published: May 16, 2017
Abstract
Ill conditioning of covariance and weight matrices used in structural equation modeling (SEM) is a possible source of inadequate performance of SEM statistics in nonasymptotic samples. A maximum a posteriori (MAP) covariance matrix is proposed for weight matrix regularization in normal theory generalized least squares (GLS) estimation. Maximum likelihood (ML), GLS, and regularized GLS test statistics (RGLS and rGLS) are studied by simulation in...
Paper Details
Title
A Regularized GLS for Structural Equation Modeling
Published Date
May 16, 2017
Volume
24
Issue
5
Pages
657 - 665
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