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数学学院、所2022年系列学术活动(第055场):陈飞 教授 云南财经大学

发表于: 2022-07-01   点击: 

报告题目:A method of local influence analysis in sufficient dimension reduction

报 告 人:陈飞 教授

所在单位:云南财经大学

报告时间:2022年7月4日 星期一 下午14:00-15:00

报告地点:腾讯会议 ID:755-805-568  会议密码:0704


报告摘要:A general framework for a local influence analysis is developed for sufficient dimension reduction when the data likelihood is absent and the inference result is a space rather than a vector. A clear and intuitive interpretation of this approach is described. Its application to the sliced inverse regression is presented, together with its invariance properties. A data trimming strategy is also suggested, based on the influence assessment for observations provided by our method. A simulation study and a real-data analysis are presented. The results indicate that the local influence analysis avoids the masking effect, and that the data trimming provides a substantial increase in the inference accuracy.


报告人简介: 陈飞,香港中文大学统计学博士,教授,博士生导师,云南财经大学统计与数学学院院长。主要从事降维理论、统计诊断、含潜变量的模型等领域的研究工作,先后主持国家自然科学基金项目4项,论文发表于Statistica Sinica, JMVA,J COMPUT GRAPH STAT,PSYCHOMETRIKA,Statistical Analysis and Data Mining等期刊。