报告题目：Online change rate learning for functional data

报告人：王文武教授曲阜师范大学

报告时间：2026年7月21日10:00-11:00

报告地点：伍卓群楼第二报告厅

校内联系人：朱复康[email protected]

报告摘要：

  With advances in modern data collection technologies, functional data are increasingly received in a streaming manner. Although numerous methods have been developed to model such data, most focus on estimating the mean and covariance functions, while giving limited attention to their derivatives. In this article, we introduce an online method for estimating the change rates of the mean and covariance functions, enabling real-time updates with high statistical efficiency. The method dynamically updates by incorporating incoming data and updating summary statistics of historical data. We establish the asymptotic normality of the estimators, classify functional data into three types based on the distributional properties (non-dense, semi-dense and ultra-dense), and moreover provide a data-driven procedure for the online bandwidth selection. By minimizing the loss of relative efficiency, we show that the proposed online change rate estimators perform comparably to the offline kernel estimators using the full data. We further compare our new estimators with representative offline and online methods through extensive simulations, demonstrating that they achieve an effective balance between statistical accuracy and computational time. Two real applications to hourly power consumption and traffic accident data, both capturing the changing trends in real time, further validate the broad applicability and substantial practical benefits of our new method.

报告人简介：

  王文武，曲阜师范大学教授，博士生导师，校统计学研究所副所长，院研究生工作负责人；香港大学博士后研究员，香港浸会大学访问学者。研究兴趣包含：非参数统计、稳健统计、机器学习与人工智能、因果推断与政策评估等。目前，主持山东省自然科学基金面上项目1项、教育部人文社科一般项目1项；主持完成国家自然科学基金面上项目1项、国家统计局重点项目1项。已发表SCI/SSCI/CSSCI论文20余篇，包括：统计学权威期刊Journal of the American Statistical Association、Statistics in Medicine、TEST、Computational Statistics & Data Analysis；机器学习与人工智能权威期刊Journal of Machine Learning Research和Knowledge-Based Systems；社会科学权威期刊Structural Equation Modeling、Psychometrika；环境统计顶级期刊Journal of Hazardous Materials；以及中文期刊《统计研究》、《数理统计与管理》等。