Some recent progress on inverse regression with count-valued predictors
报告人：Tao Wang (Shanghai Jiao Tong University)
时间：2022-09-15 14:00-15:00
地点：Tencent Meeting (438-976-078)
Abstract: 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.
About the Speaker:
王涛博士，上海交通大学数学科学学院和生命科学技术学院双聘副教授；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等期刊发表论文四十余篇；主持国家自然科学基金优秀青年科学基金。
Tencent Meeting：https://meeting.tencent.com/dm/4lhMTS2FRI8n
Meeting ID：438-976-078
