Abstract

The Wilcoxon rank sum test for two independent samples and the Kruskal–Wallis rank test for the one-way model with k independent samples are very competitive robust alternatives to the two-sample t-test and k-sample F-test when the underlying data have tails longer than the normal distribution. However, these positives for rank methods do not extend as readily to methods for making all pairwise comparisons used to reveal where the differences in location may exist. Here, we show that the closed method of Marcus et al. applied to ranks is quite powerful for both small and large samples and better than any methods suggested in the list of applied nonparametric texts found in the recent study by Richardson. In addition, we show that the closed method applied to means is even more powerful than the classical Tukey–Kramer method applied to means, which itself is very competitive for nonnormal data with moderately long tails and small samples.

摘要

为两个独立的样品和用于与单向模式秩和检验秩检验的Wilcoxon秩和检验ķ独立样本是非常有竞争力的替代健壮的两样本吨-test和ķ -sample ˚F -test当底层数据尾部比正态分布长。然而，排名方法的这些积极因素并不容易扩展到进行所有成对比较以揭示位置差异可能存在的地方的方法。在这里，我们展示了封闭方法马库斯等人。应用于等级对于小样本和大样本都非常强大，并且比理查森最近的研究中发现的应用非参数文本列表中建议的任何方法都要好。此外，我们表明应用于均值的封闭方法比应用于均值的经典 Tukey-Kramer 方法更强大，后者本身对于具有中等长尾和小样本的非正态数据非常有竞争力。