📝 SummarXiv

Abstract

Detecting simultaneous shifts in the location and scale of two populations is a challenging problem in statistical research. A common way to address this issue is by combining location and scale test statistics. A well-known example is the Lepage test, which combines the Wilcoxon-Mann-Whitney test for location with the Ansari-Bradley test for scale. However, the Wilcoxon-Mann-Whitney test assumes that the population variances are equal, while the Ansari-Bradley test assumes the population medians are equal. This study introduces new approaches that combine recent methodological advances to relax these assumptions. We incorporate the Fligner-Policello test, a distribution-free alternative to the Wilcoxon-Mann-Whitney test that does not require the assumption of equal variances. The Fligner-Policello test is further enhanced by the Fong-Huang method, which provides an improved variance estimation. Additionally, we propose a new variance estimator for the Ansari-Bradley test, thereby eliminating the need for the equal medians assumption. These methodological modifications are integrated into the Lepage framework to operate under a weak null hypothesis. Simulation results suggest that these new tests are promising candidates for location-scale testing. The practical utility of the proposed tests is then demonstrated through an analysis of four real-world biomedical datasets. These empirical applications confirm the robustness and reliability of the modified tests for the two-sample independent location-scale problem.