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Principal component analysis: A generalized Gini approach

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  • Charpentier, Arthur
  • Mussard, Stéphane
  • Ouraga, Téa

Abstract

A principal component analysis based on the generalized Gini correlation index is proposed (Gini PCA). The Gini PCA generalizes the standard PCA based on the variance. It is shown, in the Gaussian case, that the standard PCA is equivalent to the Gini PCA. It is also proven that the dimensionality reduction based on the generalized Gini correlation matrix, that relies on city-block distances, is robust to outliers. Monte Carlo simulations and an application on cars data (with outliers) show the robustness of the Gini PCA and provide different interpretations of the results compared with the variance PCA.

Suggested Citation

  • Charpentier, Arthur & Mussard, Stéphane & Ouraga, Téa, 2021. "Principal component analysis: A generalized Gini approach," European Journal of Operational Research, Elsevier, vol. 294(1), pages 236-249.
    • Handle: RePEc:eee:ejores:v:294:y:2021:i:1:p:236-249
    DOI: 10.1016/j.ejor.2021.02.010
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    1. Giovanni Giorgi, 2013. "Back to the future: some considerations on Shlomo Yitzhaki and Edna Schechtman’s book “The Gini Methodology: A Primer on a Statistical Methodology”," METRON, Springer;Sapienza Università di Roma, vol. 71(2), pages 189-195, September.
    2. Yaari, Menahem E, 1987. "The Dual Theory of Choice under Risk," Econometrica, Econometric Society, vol. 55(1), pages 95-115, January.
    3. Thibault Gajdos & John Weymark, 2005. "Multidimensional generalized Gini indices," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 26(3), pages 471-496, October.
    4. Brooks, J.P. & Dulá, J.H. & Boone, E.L., 2013. "A pure L1-norm principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 83-98.
    5. Yitzhaki, Shlomo, 1991. "Calculating Jackknife Variance Estimators for Parameters of the Gini Method," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 235-239, April.
    6. Koen Decancq & María Ana Lugo, 2013. "Weights in Multidimensional Indices of Wellbeing: An Overview," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 7-34, January.
    7. Marcel Carcea & Robert Serfling, 2015. "A Gini Autocovariance Function for Time Series Modelling," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 817-838, November.
    8. Laurini, Márcio Poletti & Ohashi, Alberto, 2015. "A noisy principal component analysis for forward rate curves," European Journal of Operational Research, Elsevier, vol. 246(1), pages 140-153.
    9. Korhonen, Pekka & Siljamaki, Aapo, 1998. "Ordinal principal component analysis theory and an application," Computational Statistics & Data Analysis, Elsevier, vol. 26(4), pages 411-424, February.
    10. 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.
    11. Charpentier, Arthur & Segers, Johan, 2009. "Tails of multivariate Archimedean copulas," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1521-1537, August.
    12. Carl Eckart & Gale Young, 1936. "The approximation of one matrix by another of lower rank," Psychometrika, Springer;The Psychometric Society, vol. 1(3), pages 211-218, September.
    13. Hofert, Marius, 2011. "Efficiently sampling nested Archimedean copulas," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 57-70, January.
    14. Banerjee, Asis Kumar, 2010. "A multidimensional Gini index," Mathematical Social Sciences, Elsevier, vol. 60(2), pages 87-93, September.
    15. Furman, Edward & Zitikis, Ričardas, 2017. "Beyond The Pearson Correlation: Heavy-Tailed Risks, Weighted Gini Correlations, And A Gini-Type Weighted Insurance Pricing Model," ASTIN Bulletin, Cambridge University Press, vol. 47(3), pages 919-942, September.
    16. 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.
    17. Shelef, Amit, 2016. "A Gini-based unit root test," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 763-772.
    18. Yaari, Menahem E., 1988. "A controversial proposal concerning inequality measurement," Journal of Economic Theory, Elsevier, vol. 44(2), pages 381-397, April.
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    1. Charles Condevaux & Stéphane Mussard & Téa Ouraga & Guillaume Zambrano, 2020. "Generalized Gini linear and quadratic discriminant analyses," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 219-236, August.
    2. Vasile Preda & Luigi-Ionut Catana, 2021. "Tsallis Log-Scale-Location Models. Moments, Gini Index and Some Stochastic Orders," Mathematics, MDPI, vol. 9(11), pages 1-22, May.

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