【专家简介】：李杰，中国人民大学统计学院讲师（团队博士后）。2022年毕业于清华大学，获统计学博士学位。主要研究方向为函数型数据分析和时间序列分析。曾获国际统计学会2021年简·丁伯根奖一等奖，国际数理统计协会2020年Hannan Graduate Student Travel Award。目前主持国家自然科学基金青年项目和中国博士后科学基金面上项目，在Statistica Sinica等期刊发表论文多篇。
【报告摘要】：We propose a novel procedure for estimating the mean function of longitudinal imaging data with inherent spatial and temporal correlation. We depict the dependence between temporally ordered images using a functional moving average, and use flexible bivariate splines over triangulations to handle the irregular domain of images which is common in imaging studies. We establish both the global and the local asymptotic properties of the bivariate spline estimator for the mean function, with simultaneous confidence corridors (SCCs) as a theoretical byproduct. Under some mild conditions, the proposed estimator and its accompanying SCCs are shown to be consistent and oracle efficient, as though all images were entirely observed without errors. We use Monte Carlo simulation experiments to demonstrate the finite-sample performance of the proposed method, the results of which strongly corroborate the asymptotic theory. The proposed method is further illustrated by analyzing two seawater potential temperature data sets.
时间：2024年04月17日 15:00 – 16:00
会议地点：崇真楼110
