Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Надійна відстань Махаланобіса× | Оцінювач Тейла-Сена× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1990 | 1968 |
| Автор методу≠ | Rousseeuw & Van Zomeren (robust distance); Filzmoser, Garrett & Reimann (multivariate outlier detection) | Henri Theil (1950); P. K. Sen (1968) |
| Тип≠ | Robust multivariate outlier detection | Robust linear regression |
| Основоположне джерело≠ | Rousseeuw, P. J. & Van Zomeren, B. C. (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association, 85(411), 633-639. DOI ↗ | Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389. DOI ↗ |
| Інші назви≠ | MCD Mahalanobis distance, robust mahalanobis, minimum covariance determinant distance, Robust Mahalanobis Uzaklığı | Theil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | Robust Mahalanobis Distance flags multivariate outliers by measuring how far each observation lies from the centre of the data using a robust covariance estimate. It builds on the robust-distance framework of Rousseeuw and Van Zomeren (1990) and the multivariate outlier-detection approach of Filzmoser, Garrett and Reimann (2005), replacing the classical mean and covariance with the Minimum Covariance Determinant (MCD) estimate so that the outliers themselves do not distort the distance. | The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%. |
| ScholarGateНабір даних ↗ |
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