统计与数据科学系系列学术报告之四百六十七期

时间： 2025年5月9日（周五）10:00-11:00

主持人：复旦大学管理学院统计与数据科学系朱仲义教授

地点：李达三楼104室

报告人：史建清教授

南方科技大学统计与数据科学系

题目：Analyzing Functional Data with a Mixture of Covariance Structures Using a Curved-Based Sampling Scheme

摘要：Motivated by distinct walking patterns in real-world free-living gait data, this paper proposes an innovative curve-based sampling scheme for the analysis of functional data characterized by a mixture of covariance structures. Traditional approaches often fail to adequately capture inherent complexities arising from heterogeneous covariance patterns across distinct subsets of the data. We introduce a unified Bayesian framework that integrates a nonlinear regression function with a continuous-time hidden Markov model, enabling the identification and utilization of varying covariance structures. One of the key contributions is the development of a computationally efficient curve-based sampling scheme for hidden state estimation, addressing the sampling complexities associated with high-dimensional, conditionally dependent data. This talk details the Bayesian inference procedure, examines the asymptotic properties to ensure the structural consistency of the model, and demonstrates its effectiveness through simulated and real-world examples.

个人简介：史建清，南方科技大学统计与数据科学系和深圳国家应用数学中心教授，理学院生物医学统计中心主任，英国皇家统计学会会士，科技部十四五重点项目主持、首席科学家。曾任英国国家艾伦图灵研究院图灵研究员，剑桥大学牛顿学院访问研究员，英国纽卡斯尔大学（Newcastle University）统计学教授，纽卡斯尔大学云计算和大数据研究中心副主任。主要研究方向包括函数型数据分析，生物医学统计，缺失数据分析，meta-analysis等。在国际学术刊物上发表高水平学术论文100多篇，包括统计和医学顶级期刊 JRSSB, JASA, Biometrika, Nature Medicine和British Medical Journal。现任J. of Computational and Graphical Statistics和Statistical Methods & Applications等期刊副主编, 曾任英国皇家统计协会《应用统计》(JRSSC)等国际期刊副主编，Guest AE for JRSS discussion paper。获IEEE康复游戏和健康国际年会最佳论文奖、美国统计协会非参数统计分会年度最佳论文奖。在Chapman & Hall 出版专著：Gaussian Process Regression Analysis for Functional Data。