[08-26] 上海交通大学王涛副教授学术报告
报告题目:Some recent progress on inverse regression with count-valued predictors
报告人:王涛副教授、博导, 上海交通大学
报告时间:8月26日上午10:00-11:00
报告地点: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.
报告人简介: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。
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