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| Робастний аналіз потужності× | Робастний однофакторний дисперсійний аналіз× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Hypothesis test | Hypothesis test |
| Рік появи≠ | 1990s–2000s | 1951 (Welch); 1990s–2000s (trimmed-mean variants) |
| Автор методу≠ | Rand R. Wilcox and colleagues | B. L. Welch; R. R. Wilcox (trimmed-mean extension) |
| Тип≠ | Power and sample-size planning | Robust parametric group comparison |
| Основоположне джерело≠ | Luh, W.-M., & Guo, J.-H. (2010). Approximate sample size formulas for the two-sample trimmed mean test with unequal variances. British Journal of Mathematical and Statistical Psychology, 63(1), 83–100. link ↗ | Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838 |
| Інші назви | power analysis under non-normality, distribution-free power analysis, robust sample-size determination, contamination-robust power | trimmed-mean ANOVA, Welch one-way ANOVA, heteroscedastic one-way ANOVA, robust ANOVA |
| Пов'язані≠ | 4 | 2 |
| Підсумок≠ | Robust power analysis computes the statistical power or required sample size for hypothesis tests that use robust estimators — such as trimmed means or Winsorized variances — instead of ordinary means and standard deviations. It protects against inflated or deflated power estimates that arise when data contain outliers, heavy tails, or skewness that violate classical normality assumptions. | 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. |
| ScholarGateНабір даних ↗ |
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