Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Kvantilregression× | Ridge Regression× | |
|---|---|---|
| Ämnesområde≠ | Ekonometri | Maskininlärning |
| Familj≠ | Regression model | Machine learning |
| Ursprungsår≠ | 1978 | 1970 |
| Upphovsperson≠ | Koenker & Bassett | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Conditional quantile regression | L2-regularized linear regression |
| Ursprungskälla≠ | Koenker, R. & Bassett, G., Jr. (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 ↗ |
| Alias≠ | conditional quantile regression, regression quantiles, Kantil Regresyon | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Närliggande≠ | 5 | 4 |
| Sammanfattning≠ | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. | 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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