Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”
Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”
复制标题
对“一种无需调整的稳健且高效的高维回归方法”的反驳
DOI：
10.1080/01621459.2020.1843865
复制
发表时间：
2020
影响因子：
3.7
通讯作者：
Wu, Yunan
中科院分区：
文献类型：
--
作者：
Wang, Lan;Peng, Bo;Bradic, Jelena;Li, Runze;Wu, Yunan
我们衷心感谢编辑Regina Liu教授和Hongyu Zhao教授对本文的特别报道和组织的启发性讨论。我们非常感谢三位杰出的讨论者对我们工作的反馈：范建青教授，洛教授和阿里少杰教授。讨论者提供了新的推理方法，提供了新的应用程序，如图形模型和因子模型，并强调了强大的程序在新的领域可能产生的影响。他们的讨论推动了强大的高维统计在不同的方向。这些与新贡献的深入讨论本身很容易成为稳健高维统计领域的独立文章。我们衷心感谢讨论者花费时间和精力，提出了有见地的意见，并慷慨地分享了他们的新发现。在下文中，我们将围绕讨论中的主要主题组织我们的答辩。
We heartily thank the editors, Professors Regina Liu and Hongyu Zhao, for featuring this article and organizing stimulating discussions. We are grateful for the feedback on our work from the three distinguished discussants: Professors Jianqing Fan, Po-Ling Loh, and Ali Shaojie. The discussants provide novel methods for inference, offer new applications such as graphical models and factor models, and highlight the possible impact of robust procedures in new domains. Their discussions have pushed forward robust high-dimensional statistics in disparate directions. These in-depth discussions with new contributions would easily qualify on their own as independent articles in the field of robust high-dimensional statistics. We sincerely thank the discussants for their time and effort in providing insightful comments and for their generosity in sharing their new findings. In the following, we organize our rejoinder around the major themes in the discussions.
影响因子：
2.7
作者：
Avella-Medina M;Battey HS;Fan J;Li Q
通讯作者：
Li Q