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| Quantilregression (nichtparametrische Varianten)× | Ridge Regression× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Maschinelles Lernen |
| Familie≠ | Regression model | Machine learning |
| Entstehungsjahr≠ | 1978 | 1970 |
| Urheber≠ | Koenker & Bassett | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Quantile regression (nonparametric variants) | L2-regularized linear regression |
| Wegweisende Quelle≠ | Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Aliasnamen | quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar) | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data. | 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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