Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Robuuste regressie× | Ridge-regressie× | |
|---|---|---|
| Vakgebied≠ | Statistiek | Machine learning |
| Familie≠ | Regression model | Machine learning |
| Jaar van ontstaan≠ | 1964 | 1970 |
| Grondlegger≠ | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) | Hoerl, A.E. & Kennard, R.W. |
| Type≠ | Regression with outlier resistance | L2-regularized linear regression |
| Oorspronkelijke bron≠ | Huber, P. J. (1964). Robust estimation of a location parameter. The 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 ↗ |
| Aliassen | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Verwant≠ | 6 | 4 |
| Samenvatting≠ | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. | 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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