Some Robust Methods of Estimation in Factor Analysis in the presence of outliers

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International Center for Advance Studies, India


A robust method of estimating covariance matrix in multivariate data set is proposed. The goal is to compare the proposed method with the widely used robust methods (Minimum Volume Ellipsoid and Minimum Covariance Determinant) and the classical method (MLE) in the area of Factor Analysis. MVE- MCD- and Proposed- factor analyses use robust covariance matrix in estimating the factor loadings while the classical factor analysis estimates the factor loadings using the MLE of the covariance matrix. While classical factor analysis is found to be more reliable when there are few or no outliers in the data, robust factor analysis will be preferred in the presence of multiple outliers. The Proposed Robust Factor analysis is found to compete favourably well with existing robust methods.



Covariance Matrix, Communalities, Factor loadings, Minimum Covariance Determinant, Minimum Volume Ellipsoid, Mean Square Error, Optimality criteria


ICASTOR Journal of Mathematical Sciences