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| Régression de Huber× | M-estimateurs (Régression Robuste)× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1964 | 2009 |
| Auteur d'origine | Peter J. Huber | Peter J. Huber |
| Type≠ | Robust linear regression (M-estimation) | Robust linear regression |
| Source fondatrice≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101. DOI ↗ | Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. link ↗ |
| Alias | Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu | m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit. |
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