A note on estimation error bound and grouping effect of Transfer Elastic Net
Abstract
The Transfer Elastic Net is an estimation method for linear regression models that combines ℓ1 and ℓ2 norms penalties to facilitate knowledge transfer. In this study, we derive a non-asymptotic ℓ2 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.
Fields of Study & Subjects
Neural Networks and Applications
Fuzzy Logic and Control Systems
Advanced Numerical Analysis Techniques
Publication details
- Journal / source
- Communication in Statistics- Theory and Methods
- Publisher
- Informa UK Limited
- Volume
- 55
- Issue
- 15
- Pages
- 4980–4999
- Publication date
- 11 March 2026
- Language
- EN