方法对比
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| 稳健重复测量方差分析× | 稳健单因素方差分析× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Hypothesis test | Hypothesis test |
| 起源年份≠ | 1990s–2000s | 1951 (Welch); 1990s–2000s (trimmed-mean variants) |
| 提出者≠ | Rand R. Wilcox | B. L. Welch; R. R. Wilcox (trimmed-mean extension) |
| 类型≠ | Robust parametric mean comparison | Robust parametric group comparison |
| 开创性文献 | Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838 | Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838 |
| 别名 | robust within-subjects ANOVA, trimmed-mean repeated measures ANOVA, robust RM-ANOVA, heteroscedastic repeated measures ANOVA | trimmed-mean ANOVA, Welch one-way ANOVA, heteroscedastic one-way ANOVA, robust ANOVA |
| 相关≠ | 6 | 2 |
| 摘要≠ | Robust repeated measures ANOVA tests whether population trimmed means differ across three or more repeated conditions or time points measured on the same subjects. By replacing ordinary means with 20% trimmed means and replacing variances with Winsorized estimates, it maintains acceptable Type I error and power when data are non-normal, skewed, or contain outliers — conditions under which classical repeated measures ANOVA routinely breaks down. | Robust one-way ANOVA compares the central tendency of three or more independent groups while resisting the distorting effects of outliers and heterogeneous variances. By replacing ordinary means with trimmed means and ordinary variances with Winsorized variances, it maintains accurate Type I error control and strong power when classical ANOVA assumptions are violated. |
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