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| Estymatory M (regresja odporna)× | Regularyzacja grzbietowa (Ridge Regression)× | |
|---|---|---|
| Dziedzina≠ | Statystyka | Uczenie maszynowe |
| Rodzina≠ | Regression model | Machine learning |
| Rok powstania≠ | 2009 | 1970 |
| Twórca≠ | Peter J. Huber | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Robust linear regression | L2-regularized linear regression |
| Źródło pierwotne≠ | Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. link ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Inne nazwy | m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | 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. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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