报告时间：2017 年1月6日下午15：00-16：00
报告地点：统计楼105室
报告人：Jun Fan 美国威斯康星大学麦迪逊分校统计系博士后、香港城市大学博士、曾在应用数学ACHA, JASA, JMLR上发表论文数篇。
报告题目：Regression Models for Functional Data via RKHS Methods
报告摘要：
Functional data analysis refers to statistical analysis of infinite dimensional data such as curves or images,as opposed to traditional multivariate analysis that deals with vectors.It becomes an important research topic in statistics because of its successful applications in neuroscience,econometrics,biomedical imaging and many other areas.In this talk,we study two types of regression models for functional data by kernel methods,including functional linear regression and functional varying coefficient model.We show that these kernel-based algorithms can achieve minimax optimal rates.