Using Scaled And Translated Measure To Compare Between Robust Estimators In Canonical Correlation

Abstract

Many researches have dealt with analysis of classical canonical correlation based on either covariance (heterogeneity) or correlation matrix where the coefficient of correlation used is Pearson which is biased to the outlier’s values, because of it depends on mean in the calculation. In our research we find robust canonical correlation depend on robust methods which is insensitive towards outliers value. Methods are used Percentage bend correlation coefficient (Pe) & Biweight midcorrelation coefficient correlation (Bi) to estimate canonical correlation (CC) instead of Pearson correlation. The researchers addressed robustness measurement to check the ability of robust methods for contaminated values, we used biased and translated estimator of empirical influence function to make the comparison between robust methods when we use simulation and choose (Bi) method to apply it on real data.