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| 稳健型Kruskal-Wallis检验× | 稳健Mann-Whitney U检验× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Hypothesis test | Hypothesis test |
| 起源年份≠ | 1952 (base); robust variants 1990s–2000s | 1947 / 2003 |
| 提出者≠ | Kruskal & Wallis (1952); robust extensions by Wilcox and others | Rand Wilcox (robust extensions); original test by Mann & Whitney (1947) |
| 类型≠ | Nonparametric robust rank-based test | Robust nonparametric two-group comparison |
| 开创性文献≠ | Mielke, P. W., & Berry, K. J. (2007). Permutation Methods: A Distance Function Approach (2nd ed.). Springer. ISBN: 978-0387698137 | Wilcox, R. R. (2005). Introduction to Robust Estimation and Hypothesis Testing (2nd ed.). Academic Press. ISBN: 978-0127515427 |
| 别名 | robust K-W test, trimmed Kruskal-Wallis, robust nonparametric one-way test, robust rank-based ANOVA | robust Wilcoxon rank-sum test, robust two-sample rank test, outlier-resistant Mann-Whitney test, robust nonparametric two-group comparison |
| 相关≠ | 3 | 1 |
| 摘要≠ | The robust Kruskal-Wallis test is a nonparametric, rank-based method for comparing three or more independent groups when data contain outliers, heavy tails, or heterogeneous spread. It augments the classical Kruskal-Wallis H statistic with robust techniques — such as trimmed means on ranks or permutation-based inference — to maintain valid Type I error rates even when distributional assumptions are violated. | The Robust Mann-Whitney U test is a nonparametric two-group comparison that combines the rank-based logic of the classic Mann-Whitney U test with modern robust techniques — such as outlier screening, trimmed means, or robust variance estimation — to produce reliable inferences when data contain extreme values, heavy-tailed distributions, or other violations that compromise the standard test. |
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