时 间：2025年5月26 日（周一）15：30 - 17：00
地 点：理科大楼A1514室
报告人：谢锦瀚 云南大学副教授
主持人：谌自奇 华东师范大学研究员
摘 要：
In this talk, we focus on the online estimation of functional linear regression with a scalar response and a functional covariate. In particular, we develop a functional stochastic gradient descent algorithm and propose an online bootstrap resampling procedure that enables us to perform the local (and global) inference for the slope function in functional linear regression. The proposed estimation and inference procedures use only one pass over the data; thus they are easy to implement and suitable to the situation where data arrive in a streaming manner. Furthermore, we establish the convergence rate and the asymptotic distribution for the proposed slope function estimator. The proposed perturbed estimator from the bootstrap procedure is shown to enjoy the same theoretical properties, which provide the theoretical justification for our online inference tool. Numerical studies are conducted to demonstrate the finite-sample performance of our proposed procedure.
报告人简介：
谢锦瀚，云南大学数学与统计学院副教授，先后在香港中文大学统计系、加拿大阿尔伯塔大学数学与统计科学系、美国北卡罗来纳大学教堂山分校生物统计系从事博士后研究工作。主要研究兴趣包括高维统计、流数据和隐私数据等。在统计学、机器学习和计量经济学等领域JASA、JMLR、JoE和JBES以及人工智能顶会ICML等发表学术论文二十余篇。