Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Robustní odhad kovariance (MCD)× | Robustní ANOVA (Welch & Ořezaný průměr)× | |
|---|---|---|
| Obor | Statistika | Statistika |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1999 | 1951 |
| Tvůrce≠ | Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD) | Welch (1951); robust trimmed-mean approach popularised by Wilcox |
| Typ≠ | Robust multivariate location-scatter estimator | Robust one-way analysis of variance |
| Původní zdroj≠ | Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223. DOI ↗ | Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336. DOI ↗ |
| Další názvy≠ | minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD) | Welch ANOVA, trimmed-mean ANOVA, heteroscedastic one-way ANOVA, Robust ANOVA (Welch & Trimmed Mean) |
| Příbuzné≠ | 4 | 5 |
| Shrnutí≠ | Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation. | Robust ANOVA compares the central tendency of three or more groups when the classical assumptions of normality and equal variances fail. It combines Welch's heteroscedasticity-adjusted statistic, introduced by Welch in 1951, with trimmed-mean tests advanced by Wilcox, giving reliable comparisons in the presence of outliers and unequal group spreads. |
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