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| Regressió Ridge Robusta× | Regressió lineal múltiple robusta× | |
|---|---|---|
| Camp | Estadística | Estadística |
| Família | Regression model | Regression model |
| Any d'origen≠ | 1991 | 1964–1980s |
| Autor original≠ | Silvapulle (1991); building on Tikhonov (1963) and Huber (1964) | Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna |
| Tipus≠ | Regularized robust linear regression | Robust linear regression |
| Font seminal≠ | Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333. link ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Àlies | ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression | robust MLR, M-estimator regression, resistant multiple regression, robust OLS |
| Relacionats≠ | 5 | 6 |
| Resum≠ | Robust Ridge regression combines M-estimation with L2 (ridge) regularization to produce coefficient estimates that are simultaneously resistant to outliers and stable under multicollinearity. It minimizes a robust loss function (such as Huber's) penalized by the squared norm of the coefficient vector, downweighting influential observations while shrinking correlated predictors toward zero. | Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients. |
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