发表机构
Beijing University of Technology（北京工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合加权IPW因子与KRR惩罚的自适应Lasso，证明其具有Oracle性质，并通过数值模拟验证其性能接近极小极大最优。
AI 中文摘要
本文首先考虑了一种包含加权IPW因子的Lasso估计器，并获得了Lasso变量选择一致或不一致的某些条件。然后，我们提供了一种配备加权IPW因子和KRR惩罚函数的自适应Lasso的改进版本，证明了所提出的自适应Lasso具有Oracle性质。数值模拟表明，所提出的自适应Lasso在性能上表现出接近极小极大最优性。
英文摘要
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
Comments17pages,9figures
