方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 分位数回归× | 岭回归（Ridge Regression）× | |
|---|---|---|
| 领域≠ | 计量经济学 | 机器学习 |
| 方法族≠ | Regression model | Machine learning |
| 起源年份≠ | 1978 | 1970 |
| 提出者≠ | Koenker & Bassett | Hoerl, A.E. & Kennard, R.W. |
| 类型≠ | Conditional quantile regression | L2-regularized linear regression |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | conditional quantile regression, regression quantiles, Kantil Regresyon | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|