报告人: 夏小超（华中农业大学）

日  期: 2019年12月20日

时  间: 14:00

地  点: 理科楼 LD202

摘  要: Screening for ultrahigh-dimensional features becomes difficult in the presence of outlying observations, heterogeneous or heavy-tailed distributions, multi-collinearity, and confounding effects. Standard correlation-based marginal screening methods may offer a weak solution to these problems. We contribute a novel robust joint screener that safeguards against outliers and distribution misspecification of both the response variable and the covariates, and accounts for external variables at the screening step. Specifically, we introduce a copula-based partial correlation (CPC) screener. We show that the empirical process of the estimated CPC converges weakly to a Gaussian process. Furthermore, we establish the sure screening property for the CPC screener under very mild technical conditions, which need not require a moment condition, and are weaker than existing alternatives in the literature. Moreover, from a theoretical perspective, our approach allows for a diverging number of conditional variables. Extensive simulation studies and two data applications demonstrate the effectiveness of the proposed screening method.

报告人简介：夏小超， 博士，华中农业大学理学院数学系讲师，2010年本科毕业于重庆大学，同年保送至本校硕博连读，2015年12月获重庆大学统计学博士学位；2013年3月-2014年1月在澳门大学数学系交流访问(Research Assistant); 2017年7月-2018年7月在新加坡国立大学统计与应用概率系从事博士后(Research Fellow)研究。目前主持1项国家自然科学青年基金项目，主持完成1项湖北省自然科学基金项目和1项中央高校基金项目并结题。感兴趣的研究方向为高维统计数据分析、超高维特征筛选、模型平均、经验似然以及非参半参数回归模型。在国际统计学知名刊物Biometrics、Statistica Sinica、Scandinavian Journal of Statistics、CSDA等多个SCI杂志上发表和录用论文十余篇，担任美国数学会数学评论（Mathematical Reviews）评论员，为多个SCI杂志提供审稿服务。

学院联系人: 张志民

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