威斯康星大学张春明教授系列学术报告3

报告题目：Semi-parametric regression for longitudinal data with regularized estimation of error covariance function

报 告 人：Chunming Zhang, Professor of Statistics, University of Wisconsin-Madison

照片：

邀 请 人：冶 继 民 教 授

报告时间：2020年6月12号上午9:00:-10:30

报告平台：Zoom ID:848 3883 6763（密码0UDgz4）

报告人简介：张春明，美国威斯康星大学国际著名统计学教授，统计学四大顶级期刊之一Annals of Statistics的前副主编、Journal of the American Statistical Association的的现任副主编。在统计学四大顶级期刊发表论文10余篇，主要研究领域有：Statistical learning & data mining；Statistical methods with applications to imaging neuroinformatics and bioinformatics；Multiple testing; large-scale simultaneous inference and applications；Statistical methods in financial econometrics；Non- and semi-parametric estimation & inference；Functional & longitudinal data analysis.

报告摘要：Improving estimation efficiency for regression coefficients is an important issue in the longitudinal data analysis, which involves modeling the error covariance structure. But challenges arise in estimating the error covariance matrix of longitudinal data collected at irregular or unbalanced time points. We develop a regularization method for estimating the error covariance function and a stepwise procedure for estimating the parametric components efficiently in the varying-coefficient partially linear model. This procedure is also applicable to the varying-coefficient temporal mixed effects model. Our method utilizes the structure of the covariance function and thus has faster rates of convergence in estimating the covariance functions and outperforms the existing approaches in simulation studies. This procedure is easy to implement and its numerical performance is investigated using both simulated and real data.