方法对比
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| 弹性网络回归× | 分位数回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2005 | 1978 |
| 提出者≠ | Hui Zou and Trevor Hastie | Koenker & Bassett |
| 类型≠ | Penalized linear regression | Conditional quantile regression |
| 开创性文献≠ | Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 别名≠ | elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 相关≠ | 6 | 5 |
| 摘要≠ | Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone. | 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. |
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