Resampling approaches were the first techniques employed to compute a variance for the
Gini coefficient; however, many authors have shown that an analysis of the Gini coefficient …

In this paper, we consider two well-known methods for analysis of the Gini index, which are
U-statistics and linearization for some income distributions. In addition, we evaluate two …

Several authors have suggested using the jackknife technique to approximate a standard
error for the Gini coefficient. It has also been shown that the Gini measure can be obtained …

This article calculates jackknife variance estimators for parameters of the Gini method. For
nonstratified samples, the calculation of the jackknife variance estimators requires only two …

We propose an Aitken estimator for Gini regression. The suggested A-Gini estimator is
proven to be a U-statistics. Monte Carlo simulations are provided to deal with …

Many government agencies still rely on the grouped data as the main source of information
for calculation of the Gini index. Previous research showed that the Gini index based on the …

The Gini coefficient has proved valuable as a measure of income inequality. In cross-
sectional studies of the Gini coefficient, information about the accuracy of its estimate is …

In this paper, we address the problem of estimating the Gini index when data are assumed
to be collected through the randomized response method proposed by Greenberg et al.(J …

Summary Since Corrado Gini suggested the index that bears his name as a way of
measuring inequality, the computation of variance of the Gini index has been subject to …

We propose a first-order bias correction term for the Gini index to reduce the bias due to
grouping. It depends on only the number of individuals in each group and is derived from a …