A note on Gini Principal Component Analysis

Téa Ouraga ()
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Téa Ouraga: Université de Nîmes - Laboratoire CHROME

Economics Bulletin, 2019, vol. 39, issue 2, 1076-1083

Abstract: 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 aff ected whereas some robustness holds with Gini PCA.

Keywords: Gini; PCA; Robutsness (search for similar items in EconPapers)
JEL-codes: C1 C4 (search for similar items in EconPapers)
Date: 2019-05-02
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