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.

翻译：迁移弹性网是一种线性回归模型的估计方法，它结合了$\ell_1$范数与$\ell_2$范数惩罚项以促进知识迁移。在本研究中，我们推导了该估计量的非渐近$\ell_2$范数估计误差界，并讨论了迁移弹性网有效发挥作用的场景。此外，我们考察了该方法呈现分组效应的情形——即高度相关的预测变量所对应的估计值具有微小差异的特性。