报告人：王涛  上海交通大学博士生导师，国家优青 

时间：4月13号（周四）下午15:00 

腾讯会议号： 827-828-819  

报告摘要：The goal of dimension reduction in regression is to reduce the dimension of the predictor space without loss of information on the regression. In many fields, the predictors of a response are count-valued, including species abundance in ecological studies, phrase tokens in text mining, and panel data in econometrics. In this talk, we review the dimension-reduction methodology in regression with count-valued predictors. We follow an inverse regression approach by modeling the conditional distribution of the predictors given the response, using the Poisson independence model and its generalizations. A new proposal is then discussed.

报告人简介：王涛博士，国家优青，上海交通大学长聘副教授，博士生导师；交大-耶鲁生物统计与数据科学联合中心研究员；国际统计学会Elected Member。研究方向为生物统计和高维数据统计推断，在JASA，JRSSB，Biometrika，Genome Biology，Briefings in Bioinformatics，Bioinformatics等期刊发表论文五十余篇；主持国家自然科学基金面上项目和优秀青年科学基金项目等多项。