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引用次数: 0
摘要
摘要 传统观点认为,基于排列的检验方法最好使用所有排列。我们对这一观点提出了质疑,因为我们发现有时使用一个很小的子群就能获得更强的能力。由于子群很小,因此计算成本也低得多。此外,这种方法对相同的假设依然有效。我们利用这一点改进了流行的基于置换的 Westfall & Young MaxT 多重检验方法。我们分析了高斯位置模型中的相对效率,发现在高维度中的收益最大。
Summary It is conventionally believed that permutation-based testing methods should ideally use all permutations. We challenge this by showing we can sometimes obtain dramatically more power by using a tiny subgroup. As the subgroup is tiny, this also comes at a much lower computational cost. Moreover, the method remains valid for the same hypotheses. We exploit this to improve the popular permutation-based Westfall & Young MaxT multiple testing method. We analyze the relative efficiency in a Gaussian location model, and find the largest gain in high dimensions.
期刊介绍:
Biometrika is primarily a journal of statistics in which emphasis is placed on papers containing original theoretical contributions of direct or potential value in applications. From time to time, papers in bordering fields are also published.