title: Fitting structural equation models using estimating equations: a model segregation approach.
abstract: Problems such as improper solution, non-convergence, subsets of variables having different distribution, and latent variables with single indicators are common in the practice of structural equation modelling. In such cases, it may be feasible to fix some model parameters at prespecified values while concentrating on estimating some other parameters. This paper formulates such a model fitting process through a model segregation approach. The statistical properties of this procedure are studied using the theory of estimating equations and optimal estimating functions. The dependency of the new parameter estimates on those of the prespecified parameter estimates is characterized for several commonly used estimating equations. A rescaled model fit statistic is proposed. Examples illustrate various applications of this procedure.
authors.affiliation: Dept of Psychology, University of Notre Dame, IN 46556, USA. kyan@nd.edu
authors.forename: Ke-Hai
authors.initials: KH
authors.lastname: Yuan
authors.forename: Wai
authors.initials: W
authors.lastname: Chan
mesh_terms: D001525:Behavioral Sciences / Q000379:methods*; D006801:Humans; D008960:Models, Psychological