期刊
ANNALS OF STATISTICS
卷 39, 期 6, 页码 3369-3391出版社
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/11-AOS946
关键词
Multiple testing under dependence; Westfall-Young procedure; permutations; familywise error rate; asymptotic optimality; high-dimensional inference; sparsity; rank-based nonparametric tests
Test statistics are often strongly dependent in large-scale multiple testing applications. Most corrections for multiplicity are unduly conservative for correlated test statistics, resulting in a loss of power to detect true positives. We show that the Westfall-Young permutation method has asymptotically optimal power for a broad class of testing problems with a block-dependence and sparsity structure among the tests, when the number of tests tends to infinity.
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