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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

What the paper says

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

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@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},
}

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

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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

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