Title（问题）：Efficient semiparametric regression for longitudinal data with nonparametric

covariance estimation

Speaker（报告人）：Prof. Yehua Li, Iowa State University

Time（时间）：2012年6月5日（周二）下昼2:00-3:00

Place（所在）：伟易博新楼217课堂

Abstract（摘要）：For longitudinal data, when the within-subject covariance is mis-specified, the semiparametric regression estimator could lose efficiency. We propose a method that combines the efficient semiparametric estimator with nonparametric covariance estimation. The proposed method is robust against mis-specification of covariance models. We show that kernel covariance estimation provides uniformly consistent estimators for the within-subject covariance matrices, and the semiparametric profile estimator with substituted nonparametric covariance is still semiparametrically efficient. The finite sample performance of the proposed estimator is illustrated by simulation studies. In an application to CD4 count data from an AIDS clinical trial, we further extend the proposed method to a functional analysis of covariance model.

About the speaker（报告人先容）：Yehua Li is Associate Professor of Statistics at the Iowa State University. He got his Ph.D. in 2006 at Texas A&M University, and worked as an assistant professor at the University of Georgia from 2006 to 2012. He moved to Iowa State University in 2012. He has published research articles in top statistics journal, such as the Annals of Statistics, Journal of the American Statistical Association and Biometrika. He has three NSF grants including a NSF CAREER award.