方法对比
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| 弹性网络回归× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2005 | 2019 |
| 提出者≠ | Hui Zou and Trevor Hastie | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Penalized linear regression | Linear 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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