Unkel, S. and Trendafilov, N. T.
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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.
|Item Type:||Journal Article|
|Copyright Holders:||2010 Elsevier B.V.|
|Keywords:||factor analysis; procrustes problems; reweighted least squares; constrained optimization; iterative majorization; robust estimation; huber function|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
|Depositing User:||Steffen Unkel|
|Date Deposited:||28 Oct 2010 11:08|
|Last Modified:||04 Oct 2016 10:47|
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