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A note on Gini Principal Component Analysis

Author

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  • Téa Ouraga

    (Université de Nîmes - Laboratoire CHROME)

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.

Suggested Citation

  • Téa Ouraga, 2019. "A note on Gini Principal Component Analysis," Economics Bulletin, AccessEcon, vol. 39(2), pages 1076-1083.
    • Handle: RePEc:ebl:ecbull:eb-19-00241
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    References listed on IDEAS

    as
    1. Shlomo Yitzhaki, 2003. "Gini’s Mean difference: a superior measure of variability for non-normal distributions," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 285-316.
    2. Shlomo Yitzhaki & Peter Lambert, 2013. "The relationship between the absolute deviation from a quantile and Gini’s mean difference," METRON, Springer;Sapienza Università di Roma, vol. 71(2), pages 97-104, September.
    3. E. Schechtman & S. Yitzhaki, 2003. "A Family of Correlation Coefficients Based on the Extended Gini Index," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 1(2), pages 129-146, August.
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    More about this item

    Keywords

    Gini; PCA; Robutsness;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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