Author:

Keywords:

Construction, Covariance determinant estimator, Data, Factor analysis, Factors, Influence function, Matrix, Maximum likelihood, Model, Multivariate analysis, Multivariate location, Outlier detection, Outliers, Principal component analysis, Robust estimation, Scatter, Scatter matrix, Science, Value, Science & Technology, Physical Sciences, Statistics & Probability, Mathematics, factor analysis, influence function, multivariate analysis, outlier detection, robust estimation, COVARIANCE DETERMINANT ESTIMATOR, PRINCIPAL COMPONENT ANALYSIS, MULTIVARIATE LOCATION, MATRIX, SCATTER, 0104 Statistics, 1403 Econometrics, 3802 Econometrics, 4905 Statistics

Abstract:

Our aim is to construct a factor analysis method that can resist the effect of outliers. For this we start with a highly robust initial covariance estimator, after which the factors can be obtained from maximum likelihood or from principal factor analysis (PFA). We find that PFA based on the minimum covariance determinant scatter matrix works well. We also derive the influence function of the PFA method based on either the classical scatter matrix or a robust matrix. These results are applied to the construction of a new type of empirical influence function (EIF), which is very effective for detecting influential data. To facilitate the interpretation, we compute a cutoff value for this EIF. Our findings are illustrated with several real data examples. (C) 2003 Elsevier Science (USA). All rights reserved.