Vertaile menetelmiä
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| Robustti kovarianssimenetelmä (MCD)× | Robust ANOVA (Welch & Trimmed Mean)× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1999 | 1951 |
| Kehittäjä≠ | Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD) | Welch (1951); robust trimmed-mean approach popularised by Wilcox |
| Tyyppi≠ | Robust multivariate location-scatter estimator | Robust one-way analysis of variance |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet≠ | 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) |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
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