A Nonparametric K-sample Test for Variability Based on Gini’s Mean Difference
Abstract 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. Simulati...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Journal of Statistical Theory and Applications (JSTA) |
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| Online Access: | https://doi.org/10.1007/s44199-025-00112-3 |
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| author | Sameera Hewage |
| author_facet | Sameera Hewage |
| author_sort | Sameera Hewage |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-e6475ff06ce54f979e88bfb038a5cf3d |
| institution | Kabale University |
| issn | 2214-1766 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of Statistical Theory and Applications (JSTA) |
| spelling | doaj-art-e6475ff06ce54f979e88bfb038a5cf3d2025-08-20T04:01:42ZengSpringerJournal of Statistical Theory and Applications (JSTA)2214-17662025-04-0124233435310.1007/s44199-025-00112-3A Nonparametric K-sample Test for Variability Based on Gini’s Mean DifferenceSameera Hewage0Department of Mathematics, University of Louisiana at LafayetteAbstract 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.https://doi.org/10.1007/s44199-025-00112-3Gini’s mean differenceVariabilityK-sample testJackknife empirical likelihood |
| spellingShingle | Sameera Hewage A Nonparametric K-sample Test for Variability Based on Gini’s Mean Difference Journal of Statistical Theory and Applications (JSTA) Gini’s mean difference Variability K-sample test Jackknife empirical likelihood |
| title | A Nonparametric K-sample Test for Variability Based on Gini’s Mean Difference |
| title_full | A Nonparametric K-sample Test for Variability Based on Gini’s Mean Difference |
| title_fullStr | A Nonparametric K-sample Test for Variability Based on Gini’s Mean Difference |
| title_full_unstemmed | A Nonparametric K-sample Test for Variability Based on Gini’s Mean Difference |
| title_short | A Nonparametric K-sample Test for Variability Based on Gini’s Mean Difference |
| title_sort | nonparametric k sample test for variability based on gini s mean difference |
| topic | Gini’s mean difference Variability K-sample test Jackknife empirical likelihood |
| url | https://doi.org/10.1007/s44199-025-00112-3 |
| work_keys_str_mv | AT sameerahewage anonparametricksampletestforvariabilitybasedonginismeandifference AT sameerahewage nonparametricksampletestforvariabilitybasedonginismeandifference |