A Note on Estimation Error Bound and Grouping Effect of Transfer Elastic Net
This work provides theoretical guarantees for a specific regularization method, but it is incremental as it builds on existing Elastic Net techniques.
The authors derived a non-asymptotic ℓ₂ norm estimation error bound for the Transfer Elastic Net estimator in linear regression and analyzed conditions for its effectiveness and grouping effect.
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.