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Measuring statistical heterogeneity: The Pietra index

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  • Eliazar, Iddo I.
  • Sokolov, Igor M.

Abstract

There are various ways of quantifying the statistical heterogeneity of a given probability law: Statistics uses variance — which measures the law’s dispersion around its mean; Physics and Information Theory use entropy — which measures the law’s randomness; Economics uses the Gini index — which measures the law’s egalitarianism. In this research we explore an alternative to the Gini index–the Pietra index–which is a counterpart of the Kolmogorov–Smirnov statistic. The Pietra index is shown to be a natural and elemental measure of statistical heterogeneity, which is especially useful in the case of asymmetric and skewed probability laws, and in the case of asymptotically Paretian laws with finite mean and infinite variance. Moreover, the Pietra index is shown to have immediate and fundamental interpretations within the following applications: renewal processes and continuous time random walks; infinite-server queueing systems and shot noise processes; financial derivatives. The interpretation of the Pietra index within the context of financial derivatives implies that derivative markets, in effect, use the Pietra index as their benchmark measure of statistical heterogeneity.

Suggested Citation

  • Eliazar, Iddo I. & Sokolov, Igor M., 2010. "Measuring statistical heterogeneity: The Pietra index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 117-125.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:1:p:117-125
    DOI: 10.1016/j.physa.2009.08.006
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    Cited by:

    1. Ghosh, Asim & Chatterjee, Arnab & Inoue, Jun-ichi & Chakrabarti, Bikas K., 2016. "Inequality measures in kinetic exchange models of wealth distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 465-474.
    2. Khosravi Tanak, A. & Mohtashami Borzadaran, G.R. & Ahmadi, J., 2017. "Maximum Tsallis entropy with generalized Gini and Gini mean difference indices constraints," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 554-560.
    3. Tsukahara, Fábio Yasuhiro & Kimura, Herbert & Sobreiro, Vinicius Amorim & Zambrano, Juan Carlos Arismendi, 2016. "Validation of default probability models: A stress testing approach," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 70-85.
    4. Patrick Krieger & Carsten Lausberg & Kristin Wellner, 2018. "Einblicke in die Gründe für nicht-normalverteilte Immobilienrenditen: eine explorative Untersuchung deutscher Wohnimmobilienportfolios [Insights into the reasons for non-normal real estate returns:," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 4(1), pages 49-79, November.
    5. Khosravi Tanak, A. & Mohtashami Borzadaran, G.R. & Ahmadi, Jafar, 2018. "New functional forms of Lorenz curves by maximizing Tsallis entropy of income share function under the constraint on generalized Gini index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 280-288.
    6. N. Nakhaei Rad & G.R. Mohtashami Borzadaran & G.H. Yari, 2016. "Maximum entropy estimation of income share function from generalized Gini index," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(16), pages 2910-2921, December.
    7. Inoue, Jun-ichi & Ghosh, Asim & Chatterjee, Arnab & Chakrabarti, Bikas K., 2015. "Measuring social inequality with quantitative methodology: Analytical estimates and empirical data analysis by Gini and k indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 184-204.
    8. Zhao, Yichuan & Su, Yueju & Yang, Hanfang, 2020. "Jackknife empirical likelihood inference for the Pietra ratio," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    9. Sarabia, José María & Jordá, Vanesa, 2014. "Explicit expressions of the Pietra index for the generalized function for the size distribution of income," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 582-595.
    10. Suchismita Banerjee & Bikas K. Chakrabarti & Manipushpak Mitra & Suresh Mutuswami, 2020. "Inequality Measures: The Kolkata index in comparison with other measures," Papers 2005.08762, arXiv.org, revised Oct 2020.
    11. Chatterjee, Arnab & Ghosh, Asim & Chakrabarti, Bikas K., 2017. "Socio-economic inequality: Relationship between Gini and Kolkata indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 583-595.
    12. Ruz, Soumendra Nath, 2023. "Amazing aspects of inequality indices (Gini and Kolkata Index) of COVID-19 confirmed cases in India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P2).
    13. Khosravi Tanak, A. & Mohtashami Borzadaran, G.R. & Ahmadi, J., 2015. "Entropy maximization under the constraints on the generalized Gini index and its application in modeling income distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 657-666.
    14. Francesco Porro & Mariangela Zenga, 2023. "Decompositions by sources and by subpopulations of the Pietra index: two applications to professional football teams in Italy," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 73-100, March.
    15. Banerjee, Suchismita & Chakrabarti, Bikas K. & Mitra, Manipushpak & Mutuswami, Suresh, 2020. "On the Kolkata index as a measure of income inequality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    16. Dashti Moghaddam, M. & Mills, Jeffrey & Serota, R.A., 2020. "From a stochastic model of economic exchange to measures of inequality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    17. Gómez-Déniz, Emilio & Dorta-González, Pablo, 2024. "Modeling citation concentration through a mixture of Leimkuhler curves," Journal of Informetrics, Elsevier, vol. 18(2).

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