報告人🏜：王濤  上海交通大學博士生導師，國家優青 

時間：4月13號（周四）下午15:00 

騰訊會議號： 827-828-819  

報告摘要#️⃣🧘🏼：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.

報告人簡介🧏🏽‍♂️：王濤博士🥇，國家優青，上海交通大學長聘副教授，博士生導師；交大-耶魯生物統計與數據科學聯合中心研究員；國際統計學會Elected Member。研究方向為生物統計和高維數據統計推斷🕺🏽，在JASA🧛🏼‍♂️，JRSSB，Biometrika🍚，Genome Biology♜，Briefings in Bioinformatics🏂🏻，Bioinformatics等期刊發表論文五十余篇🛂；主持國家自然科學基金面上項目和優秀青年科學基金項目等多項。