Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| S-оцінювач для робастного регресійного аналізу× | MM-оцінювання для робастного регресійного аналізу× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1984 | 1987 |
| Автор методу≠ | Rousseeuw & Yohai (1984) | Victor J. Yohai |
| Тип | Robust linear regression | Robust linear regression |
| Основоположне джерело≠ | Rousseeuw, P. J. & Yohai, V. J. (1984). Robust Regression by Means of S-Estimators. In Robust and Nonlinear Time Series Analysis (Lecture Notes in Statistics, Vol. 26, pp. 256-272). Springer. DOI ↗ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ |
| Інші назви≠ | S-estimation, robust S-regression, S-Tahmin Edici | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici |
| Пов'язані | 5 | 5 |
| Підсумок≠ | The S-estimator is a robust linear-regression method, introduced by Rousseeuw and Yohai in 1984, that estimates the coefficients by minimising a robust M-estimate of the residual scale rather than the variance of the residuals. By driving down a bounded measure of residual spread it can attain a breakdown point of up to 50%, so it stays reliable even when a large share of the data are outliers, and it provides the first stage of the well-known MM-estimator. | The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved. |
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