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| Huberregression× | MM-estimering för robust regression× | |
|---|---|---|
| Ämnesområde | Statistik | Statistik |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 1964 | 1987 |
| Upphovsperson≠ | Peter J. Huber | Victor J. Yohai |
| Typ≠ | Robust linear regression (M-estimation) | Robust linear regression |
| Ursprungskälla≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101. DOI ↗ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ |
| Alias | Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit. | 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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