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A non-parametric maximum test for the Behrens-Fisher problem
Welz Anke
Ruxton Graeme D
Neuhaeuser Markus
Journal of Statistical Computation and Simulation, 2018, 88(7): 1336-1347.
Non-normality and heteroscedasticity are common in applications. For the comparison of two samples in the non-parametric Behrens-Fisher problem, different tests have been proposed, but no single test can be recommended for all situations. Here, we propose combining two tests, the Welch t test based on ranks and the Brunner-Munzel test, within a maximum test. Simulation studies indicate that this maximum test, performed as a permutation test, controls the type I error rate and stabilizes the power. That is, it has good power characteristics for a variety of distributions, and also for unbalanced sample sizes. Compared to the single tests, the maximum test shows acceptable type I error control.
Behrens-Fisher problem; Brunner-Munzel test; maximum test; Welch t test
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