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)  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)
Omar, Tami; Abdelaziz, Rassoul; and Rouis Hamid, Ould
"An Improved Estimator of the Zenga Index for Heavy-Tailed Distributions,"
Journal of Statistics Applications & Probability: Vol. 8
, Article 3.
Available at: https://dc.naturalspublishing.com/jsap/vol8/iss2/3