arbiter
JournalsAccount

A note on estimation error bound and grouping effect of Transfer Elastic Net

Yui Tomo

Communications in Statistics: Theory and Methods2026https://doi.org/10.1080/03610926.2026.2626155preprint
ABDC B
Weight
0.50

Abstract

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.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1080/03610926.2026.2626155

Or copy a formatted citation

@article{yui2026,
  title        = {{A note on estimation error bound and grouping effect of Transfer Elastic Net}},
  author       = {Yui Tomo},
  journal      = {Communications in Statistics: Theory and Methods},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/03610926.2026.2626155},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

A note on estimation error bound and grouping effect of Transfer Elastic Net

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.