A Nonparametric K-sample Test for Variability Based on Gini’s Mean Difference

In this study, we utilize Gini’s mean difference (GMD) to develop a nonparametric test for comparing variability across K populations. A jackknife empirical likelihood (JEL) method was applied to develop the test statistic, with a chi-squared distribution of K-1 degrees of freedom. Simulation studies were conducted to evaluate the performance of the proposed approach with respect to empirical size and test power. Finally, the methods were illustrated using two real datasets. Both simulation studies and real data analysis show that the proposed methods have better performance across a variety of settings.