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Inequality measures for histograms


  • Benito V. Frosini

    (Istituto di Statistica - Università Cattolica del Sacro Cuore di Milano)


While inequality or concentration measures are defined with reference to the distribution of a non-negative character among n individuals, most practical applications are effected on frequency distributions over k classes, namely on histograms, when thinking of the corresponding graphical representation. Concerning this type of applications, this paper examines: (1) the goodness of approximation – to indices computed on individual data – of the same indices worked out on histograms; (2) the meaning and properties of inequality indices that are functions of the only frequencies and quantities pertaining to the k classes. These two kinds of investigations have been addressed to classical concentration measures proposed by Gini and Pietra-Ricci.

Suggested Citation

  • Benito V. Frosini, 2005. "Inequality measures for histograms," Statistica, Department of Statistics, University of Bologna, vol. 65(1), pages 27-40.
  • Handle: RePEc:bot:rivsta:v:65:y:2005:i:1:p:27-40

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    Cited by:

    1. Benito Frosini, 2012. "Approximation and decomposition of Gini, Pietra–Ricci and Theil inequality measures," Empirical Economics, Springer, vol. 43(1), pages 175-197, August.
    2. E. Gómez-Déniz, 2016. "A family of arctan Lorenz curves," Empirical Economics, Springer, vol. 51(3), pages 1215-1233, November.

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