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International Journal of Statistika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605
Volume 11, Issue 2, August 2014 pp 53-58
Factor analysis based on classical and robust estimators
Muthukrishnan R1, E D Boobalan2
1,2Department of Statistics, Bharathiar University, Coimbatore-46 Tamil Nadu, INDIA.
Academic Editor: Dr. Dase R. K.
Introduction: The multivariate methods, such as principal component analysis, discriminant analysis, cluster analysis, multivariate regressions etc., are mainly based on the empirical measures mean vector, covariance and correlation matrices. All these measures strongly affected by even a single outliers present in the multivariate data set. Robust alternatives measures are established to overcome this limitation. Many multivariate robust procedures are established to estimate these measures. All these robust procedures established based on the sample of selecting the best observations (which represents the original data) nearly half of the data points. Among these, the minimum covariance determinant estimator (MCD) proposed by Rousseeuw (1984) is one of the highly robust estimators of estimating multivariate location and scatter. This paper provides an attempt to explore such robust procedures along with the application in factor analysis. Further it is proposed to construct robust factor analysis with the help of most widely used robust methods MVE, S and MM that can resist the effect of outliers. The efficiency of these estimators with classical one is carried out by providing an empirical study with a help of MATLAB software.