统计学系系列讲座之331期

 

时 间:2019年3月20日(星期三)14:30-15:30

地 点:史带楼205室

主持人:朱仲义 教授 复旦大学管理学院统计学系

主 题:A Generic Sure Independence Screening Procedure

主讲人:王学钦 教授  中山大学数学与统计学院

简 介:王学钦博士现为中山大学数学学院和中山医学院教授,担任中山大学统计学科带头人,数学学院院长助理,中山大学华南统计科学研究中心执行主任等职。2003年毕业于纽约州立大学宾厄姆顿分校(Binghamton University), 2012年入选教育部新世纪优秀人才支持计划学者, 2013年获得国家优秀青年研究基金,2014年入选第八批广东省高等学校“千百十工程”国家级培养计划,2016年入选“广东特支计划”(百千工程领军人才)。此外,他还担任教育部高等学校统计学类专业教学指导委员会委员、统计学国际期刊JASA(ACS)、《SII》、《JCS》的副主编和高等教育出版社《Lecture Notes: Data Science, Statistics and Probability》系列丛书的副主编等。

主要从事精准医疗、非参数统计、和机器学习等方面的研究。目前已经在包括统计学顶级刊物AOS、JASA和nature genetics等在内的国际学术期刊上发表SCI论文60余篇。

摘 要:Extracting important features from ultra-high dimensional data is one of the primary tasks in statistical learning, information theory, precision medicine, and biological discovery. Many of the sure independent screening methods developed to meet these needs are suitable for special models under some assumptions. With the availability of more data types and possible models, a model-free generic screening procedure with fewer and less restrictive assumptions is desirable. In this article, we propose a generic nonparametric sure independence screening procedure, called BCor-SIS, on the basis of a recently developed universal dependence measure: Ball correlation. We show that the proposed procedure has strong screening consistency even when the dimensionality is an exponential order of the sample size without imposing sub-exponential moment assumptions on the data. We investigate the flexibility of this procedure by considering three commonly encountered challenging settings in biological discovery or precision medicine: iterative BCor-SIS, interaction pursuit, and survival outcomes. We use simulation studies and real data analyses to illustrate the versatility and practicability of our BCor-SIS method. Supplementary materials for this article are available online.

                       

 统计学系 

2019-3-19