An Improved Estimator of the Zenga Index for Heavy-Tailed Distributions

Authors

Tami Omar, LAMDARO Laboratory, Departement of Mathematics, University of Blida 1, Algeria
Rassoul Abdelaziz
Ould Rouis Hamid

Author Country (or Countries)

Algeria

Abstract

In the present paper, we focus on the Zenga index, the asymptotic normality of the classical estimators has been established in the literature under the classical assumption that the second moment of the loss variable is finite, this condition is very restrictive in practical applications. Such a result has been extended by Greselin et al. (2014) [31] in the case of distributions with infinite second moment. Thus, we base on this framework and propose a reduced-bias estimator for the classical estimators. Finally, we illustrate the efficiency of our approach by some results on a simulation study and compare its performance with other estimators.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/jsap/080203

Recommended Citation

Omar, Tami; Abdelaziz, Rassoul; and Rouis Hamid, Ould (2019) "An Improved Estimator of the Zenga Index for Heavy-Tailed Distributions," Journal of Statistics Applications & Probability: Vol. 8: Iss. 2, Article 3.
DOI: http://dx.doi.org/10.18576/jsap/080203
Available at: https://digitalcommons.aaru.edu.jo/jsap/vol8/iss2/3