IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-19-00241.html
   My bibliography  Save this article

A note on Gini Principal Component Analysis

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2019/Volume39/EB-19-V39-I2-P102.pdf
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ndéné Ka & Stéphane Mussard, 2016. "ℓ 1 regressions: Gini estimators for fixed effects panel data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1436-1446, June.
    2. Arthur Charpentier & Stéphane Mussard & Tea Ouraga, 2019. "Principal Component Analysis: A Generalized Gini Approach," Working Papers hal-02327521, HAL.
    3. Klein, Ingo & Mangold, Benedikt, 2015. "Cumulative Paired ," FAU Discussion Papers in Economics 07/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    4. Stéphane Mussard & Fattouma Souissi-Benrejab, 2019. "Gini-PLS Regressions," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 477-512, September.
    5. Shlomo Yitzhaki & Peter Lambert, 2014. "Is higher variance necessarily bad for investment?," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 855-860, November.
    6. 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.
    7. Ndene Ka & Stephane Mussard, 2015. "l1 Regressions: Gini Estimators for Fixed Effects Panel Data," Cahiers de recherche 15-02, Departement d'Economique de l'École de gestion à l'Université de Sherbrooke.
    8. 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.
    9. Yoel Finkel & Yevgeny Artsev & Shlomo Yitzhaki, 2006. "Inequality measurement and the time structure of household income in Israel," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 4(2), pages 153-179, August.
    10. William Horrace & Joseph Marchand & Timothy Smeeding, 2008. "Ranking inequality: Applications of multivariate subset selection," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(1), pages 5-32, March.
    11. N. Nair & P. Sankaran & B. Vineshkumar, 2012. "Characterization of distributions by properties of truncated Gini index and mean difference," METRON, Springer;Sapienza Università di Roma, vol. 70(2), pages 173-191, August.
    12. Carina Gerstenberger & Daniel Vogel, 2015. "On the efficiency of Gini’s mean difference," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 569-596, November.
    13. Miguel A. Lejeune & John Turner, 2019. "Planning Online Advertising Using Gini Indices," Operations Research, INFORMS, vol. 67(5), pages 1222-1245, September.
    14. Erreygers, Guido, 2009. "Can a single indicator measure both attainment and shortfall inequality?," Journal of Health Economics, Elsevier, vol. 28(4), pages 885-893, July.
    15. M. Grazia Pittau & Shlomo Yitzhaki & Roberto Zelli, 2015. "The “Make-up” of a Regression Coefficient: Gender Gaps in the European Labor Market," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(3), pages 401-421, September.
    16. Vanderford Courtney & Sang Yongli & Dang Xin, 2020. "Two symmetric and computationally efficient Gini correlations," Dependence Modeling, De Gruyter, vol. 8(1), pages 373-395, January.
    17. Reza Mousavi & Monica Johar & Vijay S. Mookerjee, 2020. "The Voice of the Customer: Managing Customer Care in Twitter," Information Systems Research, INFORMS, vol. 31(2), pages 340-360, June.
    18. Edna Schechtman & Shlomo Yitzhaki & Taina Pudalov, 2011. "Gini’s multiple regressions: two approaches and their interaction," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 67-99.
    19. Le Gal La Salle, Josselin & Badosa, Jordi & David, Mathieu & Pinson, Pierre & Lauret, Philippe, 2020. "Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts," Renewable Energy, Elsevier, vol. 162(C), pages 1321-1339.
    20. Shlomo Yitzhaki & Edna Schechtman, 2004. "The Gini Instrumental Variable, or the “double instrumental variable” estimator," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 287-313.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ebl:ecbull:eb-19-00241. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: John P. Conley (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.