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Fitting structural equation models using estimating equations: A model segregation approach
Author(s) -
Yuan KeHai,
Chan Wai
Publication year - 2002
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711002159699
Subject(s) - mathematics , statistic , dependency (uml) , convergence (economics) , structural equation modeling , estimation theory , mathematical optimization , statistics , computer science , software engineering , economics , economic growth
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.