Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Robust multipel linjär regression× | Ridge Regression× | |
|---|---|---|
| Ämnesområde≠ | Statistik | Maskininlärning |
| Familj≠ | Regression model | Machine learning |
| Ursprungsår≠ | 1964–1980s | 1970 |
| Upphovsperson≠ | Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Robust linear regression | L2-regularized linear regression |
| Ursprungskälla≠ | Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Alias | robust MLR, M-estimator regression, resistant multiple regression, robust OLS | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Närliggande≠ | 6 | 4 |
| Sammanfattning≠ | 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. | 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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