数学与统计学院学术报告[2020] 101号

(高水平大学建设系列报告454号)

报告题目:  Online Estimation for Functional Data

报告人：姚方教授（北京大学）

报告时间：2020年11月19日周四下午14:00-15:00　　　　　　　　　　　　 　　

报告地点：腾讯会议 会议号码：326431743　 　　　　　　　　　　　　

报告内容：Functional data analysis has attracted considerable interest, and is facing new challenges of the increasingly available data in streaming manner. In this work, we propose a new online method to dynamically update the local linear estimates of mean and covariance functions of functional data, which is the foundation of subsequent analysis. The kernel-type estimates can be decomposed into two sufficient statistics depending on the data-driven bandwidths. We propose to approximate the future optimal bandwidths by a dynamic sequence of candidates and combine the corresponding statistics across blocks to make an updated estimation. The proposed online method is easy to compute based on the stored sufficient statistics and current data block. Based on the asymptotic normality of the online mean and covariance function estimates, the relative efficiency in terms of integrated mean squared error is studied and a theoretical lower bound is obtained. This bound provides insight into the relationship between estimation accuracy and computational cost driven by the length of candidate bandwidth sequence that is pivotal in the online algorithm. Simulations and real data applications are provided to 报告人简历:  姚方,北京大学讲席教授、北大统计科学中心主任，数理统计学会（IMS）Fellow与理事会理事，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，曾任职于多伦多大学统计科学系长聘正教授。现担任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。

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数学与统计学院

2020年11月16日

（本文最近更新于2020/11/16 17:18:00　累计点击数:86）