Téa Ouraga   ''A note on Gini Principal Component Analysis'' ( 2019, Vol. 39 No.2 )     In this paper, a principal component analysis based on the Gini index - Gini PCA - is proposed in order to deal with contaminated samples. The operator underlying the Gini index is a covariance-based operator, which provides a l1 metric well suited for dealing with outliers. It is shown, with simple Monte Carlo experiments, that the results of the standard Principal Component Analysis (PCA) may be drastically affected whereas some robustness holds with Gini PCA.     Keywords: Gini, PCA, Robutsness JEL: C1 - Econometric and Statistical Methods: General C4 - Econometric and Statistical Methods: Special Topics   Manuscript Received : Mar 07 2019   Manuscript Accepted : May 02 2019   This abstract has been downloaded 1121 times                The Full PDF of this paper has been downloaded 166339 times