Regression modelRegression / GLM
弹性网络回归
弹性网络回归将 L1 (套索) 和 L2 (岭回归) 惩罚项结合到一个单一的正则化回归框架中。通过混合参数 alpha 和收缩强度 lambda 控制，它可以同时进行变量选择并处理相关预测变量——克服了单独应用纯套索或纯岭回归的关键局限性。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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: 10.1111/j.1467-9868.2005.00503.x ↗
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. ISBN: 978-0387848570
如何引用本页
ScholarGate. (2026, June 3). Elastic Net Regularized Regression. ScholarGate. https://scholargate.app/zh/statistics/elastic-net-regression
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Lasso 回归机器学习↔ compare
- 普通最小二乘法 (OLS) 回归计量经济学↔ compare
- 分位数回归计量经济学↔ compare
- 正则化逻辑回归机器学习↔ compare
- 岭回归（Ridge Regression）机器学习↔ compare
- 稳健回归统计学↔ compare