報告題目：A method of local influence analysis in sufficient dimension reduction
報 告 人：陳飛 教授
所在單位：雲南财經大學
報告時間：2022年7月4日 星期一 下午14:00-15:00
報告地點：騰訊會議 ID：755-805-568 會議密碼：0704
報告摘要：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.
報告人簡介： 陳飛，香港中文大學統計學博士，教授，博士生導師，雲南财經大學統計與伟德线上平台院長。主要從事降維理論、統計診斷、含潛變量的模型等領域的研究工作，先後主持國家自然科學基金項目4項，論文發表于Statistica Sinica, JMVA，J COMPUT GRAPH STAT，PSYCHOMETRIKA，Statistical Analysis and Data Mining等期刊。
