提出迁移弹性网络的误差界与分组效应分析，揭示其在高相关变量下的稳定表现。
A Note on Estimation Error Bound and Grouping Effect of Transfer Elastic Net
- 结合L1和L2正则化实现知识迁移，提升回归估计精度
- 给出非渐近l2误差上界，量化模型稳定性
- 在高度相关特征下具分组效应，适合结构化变量建模
迁移弹性网络是一种结合ℓ1和ℓ2范数惩罚的线性回归估计方法，用于实现知识迁移。本文推导了该估计器的非渐近ℓ2范数估计误差界，并讨论了其有效适用场景。此外，研究还分析了其表现出分组效应的情形，即高度相关的预测变量对应的估计值差异较小。
原文摘要 · Abstract (English)
The Transfer Elastic Net is an estimation method for linear regression models that combines $\ell_1$ and $\ell_2$ norm penalties to facilitate knowledge transfer. In this study, we derive a non-asymptotic $\ell_2$ norm estimation error bound for the estimator and discuss scenarios where the Transfer Elastic Net effectively works. Furthermore, we examine situations where it exhibits the grouping effect, which states that the estimates corresponding to highly correlated predictors have a small difference.
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