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Efficient semiparametric regression for longitudinal data with nonparametric covariance estimation

报告题目:Efficient semiparametric regression for longitudinal data with nonparametric covariance estimation

 

报告人:Yehua Li, Iowa StateUniversity.

 

时间:201266(星期三)16:00-17:00

 

地点:理科楼数学系A304

 

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.

 

报告人简介:李业华博士2000年毕业于365体育官网app下载数学系,获学士学位。2006年在美国Texas A&M University获得博士学位。2006年至2012年在University of Georgia 任助理教授。2012年任Iowa State University的副教授。李业华博士的主要从事统计方面的研究。他在统计学的顶尖杂志:Annals of Statistics, Journal of the American Statistical Association and Biometrika等发表多篇学术论文。

 

联系人:殷东生

 

 

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