Power improvements and exact shortcuts for closed permutation maxT testing with discrete data

Abstract

In permutation tests with discrete data, especially in small samples, different permutations often yield the same test statistic. The resulting ties can make the test conservative and reduce power. This conservatism can be particularly pronounced for maximum statistics: permutations may have the same maximum but differ at other endpoints, so the remaining endpoint statistics are discarded. We develop two power improvements for closed permutation maxT tests by refining the treatment of ties. The first method uses a single uniform random variable shared across all local tests in the closed testing procedure. This shared-uniform construction maximizes the rejection probability of every elementary hypothesis among all ways of coordinating the same randomized local tests. It also preserves an exact linear step-down shortcut. The second method is the deterministic two-endpoint augmentation (tea) test, which uses the second-largest endpoint statistic to resolve ties in the maximum. This additional information can favour rejection under alternatives while preserving null validity, without auxiliary randomization. Its full closure admits an exact quadratic shortcut. More generally, augmentation using any fixed number of endpoint statistics admits an exact polynomial shortcut. Both procedures retain every rejection made by classical closed maxT procedures and provide strong finite-sample control of the family-wise error rate under intersection-null permutation invariance. In simulations with sparse binary outcomes, both methods improve power over classical closed maxT testing. When many endpoints are tested, the tea test also often outperforms shared-uniform randomization.