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The Adaptive Lasso with Weighted IPW Factor and KRR Penalty Function

  • Zeen Song
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Abstract

In this paper, we first consider a Lasso estimator incorporating a weighted IPW factor,and obtain certain conditions for the Lasso variable selection to be consistent or inconsistent. Then we provide a modified version of the adaptive Lasso equipped with a weighted IPW factor and a KRR penalty function, establishing that the proposed adaptive Lasso possesses the Oracle property. Numerical simulations demonstrate that the proposed adaptive Lasso exhibits near-minimax optimality in performance

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Cite

BibTeXRIS
BibTeX
@misc{song2026adaptive,
  title = {{The Adaptive Lasso with Weighted IPW Factor and KRR Penalty Function}},
  author = {Zeen Song},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.08805},
  eprint = {2610.08805},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.08805},
}
RIS
TY  - UNPB
TI  - The Adaptive Lasso with Weighted IPW Factor and KRR Penalty Function
AU  - Zeen Song
PY  - 2026
DA  - 2026/10/01
PB  - arXiv
DO  - 10.48550/arxiv.2610.08805
UR  - https://arxiv.org/abs/2610.08805
AB  - In this paper, we first consider a Lasso estimator incorporating a weighted IPW factor,and obtain certain conditions for the Lasso variable selection to be consistent or inconsistent. Then we provide a modified version of the adaptive Lasso equipped with a weighted IPW factor and a KRR penalty function, establishing that the proposed adaptive Lasso possesses the Oracle property. Numerical simulations demonstrate that the proposed adaptive Lasso exhibits near-minimax optimality in performance
ER  -