报告题目:Some recent progress on inverse regression with count-valued predictors
主讲人:王涛
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.
王涛简介:上海交通大学,长聘教轨副教授,博士生导师.香港浸会大学统计学博士,曾在美国耶鲁大学公共卫生学院从事博士后研究工作,是国际统计学会当选会员和美国耶鲁大学生物统计系客座助理教授.主要研究方向包括高维数据统计降维、生物医学数据挖掘与分析、以及潜变量建模与推断.
报告时间:2020年12月14日下午3:30-4:30
报告地点:腾讯会议ID:465354512 会议密码:2020
链接:https://meeting.tencent.com/s/2lWkEdqTJ8Iq
主办单位:数学与统计学院
