A majorization algorithm for simultaneous parameter estimation in robust exploratory factor analysis

S. Unkel and N.T. Trendafilov

Computational Statistics & Data Analysis, 2010, vol. 54, issue 12, 3348-3358

Abstract: A new approach for fitting the exploratory factor analysis (EFA) model is considered. The EFA model is fitted directly to the data matrix by minimizing a weighted least squares (WLS) goodness-of-fit measure. The WLS fitting problem is solved by iteratively performing unweighted least squares fitting of the same model. A convergent reweighted least squares algorithm based on iterative majorization is developed. The influence of large residuals in the loss function is curbed using Huber's criterion. This procedure leads to robust EFA that can resist the effect of outliers in the data. Applications to real and simulated data illustrate the performance of the proposed approach.

Keywords: Factor; analysis; Procrustes; problems; Reweighted; least; squares; Constrained; optimization; Iterative; majorization; Robust; estimation; Huber; function (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (10)

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