报告人：王涛副教授、博导, 上海交通大学
时间：8月26号上午10-11点
腾讯会议号：269-174-222
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 briefly discussed.
个人简介：王涛博士，上海交通大学统计系、生物信息和生物统计系长聘副教授/博导，交大-耶鲁生物统计与数据科学联合中心研究员。东南大学学士、华东师范大学硕士、香港浸会大学博士、美国耶鲁大学博士后；国际统计学会Elected Member、耶鲁大学生物统计系客座助理教授；获国家优青和2021年度上海市生物信息学会青年新星奖。研究方向为生物统计和高维数据统计推断，主要成果发表在JASA，JRSSB，Biometrika，Genome Biology，Briefings in Bioinformatics，Bioinformatics等。
