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Robust mean and covariance structure analysis through iteratively reweighted least squares

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Abstract

Robust schemes in regression are adapted to mean and covariance structure analysis, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is properly weighted according to its distance, based on first and second order moments, from the structural model. A simple weighting function is adopted because of its flexibility with changing dimensions. The weight matrix is obtained from an adaptive way of using residuals. Test statistic and standard error estimators are given, based on iteratively reweighted least squares. The method reduces to a standard distribution-free methodology if all cases are equally weighted. Examples demonstrate the value of the robust procedure.

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Authors and Affiliations

  1. Department of Psychology, University of North Texas, PO Box 311280, 76203-1280, Denton, TX

    Ke-Hai Yuan

  2. University of California, Los Angeles

    Peter M. Bentler

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  1. Ke-Hai Yuan
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  2. Peter M. Bentler
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Additional information

The authors acknowledge the constructive comments of three referees and the Editor that lead to an improved version of the paper. This work was supported by National Institute on Drug Abuse Grants DA01070 and DA00017 and by the University of North Texas Faculty Research Grant Program.

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Yuan, KH., Bentler, P.M. Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika 65, 43–58 (2000). https://doi.org/10.1007/BF02294185

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