A nonparametric feature screening method for ultrahigh-dimensional missing response

发布时间：2021-06-01 作者：77779193永利官网浏览次数：

Speaker:	唐年胜	DateTime:	2021年6月4日（周五）下午17:00-17:50
Brief Introduction to Speaker: 唐年胜，国家杰出青年科学基金获得者，云南大学数学与统计学院教授，教育部“新世纪优秀人才”，云南省科技领军人才，云南省首批云岭学者，云南省中青年学术和技术带头人，云南省教学名师，云南省学位委员会经济与管理学科评议组成员，博士生导师。云南省高校“统计与信息技术重点实验室 ”负责人，“云南大学复杂数据统计推断方法研究 ”省创新团队带头人。		Place:	六号楼二楼报告厅
Abstract:This paper addresses the feature screening issue for ultrahigh-dimensional data with responses missing at random. A novel nonparametric feature screening procedure is developed to identify the important features via the conditionally imputing marginal Spearman rank correlation. The proposed nonparametric screening approach has several desirable merits. First, it is nonparametric without assuming any regression form of predictors on response variable. Second, it is robust to outliers and heavy-tailed data. Third, under some regularity conditions, it is shown that the proposed feature screening procedure has the sure screening and ranking consistency properties. Simulation studies evidence that the proposed screening procedure outperforms several existing model-free screening procedures. An example taken from the microarray diffuse large-B-cell lymphoma study is used to illustrate the proposed methodologies.

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