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Model fitting procedures for nonlinear factor analysis using the errors-in-variables parameterization

  • Yasuo Amemiya
  • Ilker Yalcin
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 120)

Abstract

Traditional factor analysis and structural equation modeling use models which are linear in latent variables. Here, a general parametric nonlinear factor analysis model is introduced. The identification problem for the model is discussed, and the errors-invariables parametrization is proposed as a solution. Two general procedures for fitting the model are described. Tests for the goodness of fit of the model are also proposed. The usefulness and comparison of the model fitting procedures are studied based on a simulation.

Keywords

Nonlinear Factor Error Variance Estimate Factor Vector Approximate Likelihood Maximum Normal Likelihood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1999

Authors and Affiliations

  • Yasuo Amemiya
    • 1
  • Ilker Yalcin
    • 1
  1. 1.Iowa State UniversityUSA

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