Abstract: Functional data analysis (FDA) has received substantial attention, with applications arising from various disciplines, such as engineering, public health, finance etc. In general, the FDA approaches focus on nonparametrically modeling underlying random functions, which treats the data as observed or sampled from realizations of stochastic processes satisfying some regularity conditions, e.g., smoothness constraints. The estimation and inference procedures usually do not depend on a finite number of parameters, which contrasts with parametric models, and exploit techniques, such as smoothing methods and dimension reduction, that allow data to speak for themselves. In this talk, I will give an overview of FDA methods and related topics developed in recent years.


主讲人简介：姚方， 北京大学讲席教授、统计科学中心主任，国家特聘专家，数理统计学会（IMS）Fellow，美国统计学会（ASA）Fellow。2000年本科毕业于中国科技大学统计专业，2003获得加利福尼亚大学戴维斯分校统计学博士学位，2014年获加拿大数学会与统计学会联合颁发的CRM-SSC Prize，曾任职于多伦多大学统计科学系终身教授。现担任IMS理事会理事与《Canadian Journal of Statistics》的主编，至今担任9个国际统计学核心期刊编委，包括统计学顶级期刊Journal of the American Statistical Association和 Annals of Statistics。