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。
