数学与系统科学研究院 统计科学研究中心

Center for Statistics Science, AMSS, CAS

 

学术交流会议

学术报告

 

 

 

 

n       国际统计前沿会议——高维数据分析(2007.8.13-15

 近期学术报告:

 

n       第八届全国概率统计会议征文第一通知 ( 2006.10.27-31)

报告人 

Runze Li:  Associate Professor  

The Pennsylvania State University

 

n       International Conference on Frontiers of Statistics - Biostatistics and Bioinformatics(2006.7.7-8

题目   

Analysis of Longitudinal Data with

n       厦门大学王亚南经济研究院与中科院数学系统科学研究院将联合举办“金融工程与风险管理” 国际研讨会(2006.7.5-6

 

Semiparametric Estimation of Covariance Function

 

时间   

20070518(周五)下午3:30-4:30

 

地点   

晨兴中心605

 

摘要

Improving efficiency for regression coefficients and predicting trajectories of individuals are two important aspects in analysis of longitudinal data. Both involve estimation of the covariance function. Yet, challenges arise in estimating the covariance function of longitudinal data collected at irregular time points. A class of semiparametric models for the covariance function is proposed by imposing a parametric correlation structure while allowing a nonparametric variance function. A kernel estimator is developed for the estimation of the nonparametric variance function. Two methods, a quasi-likelihood approach and a minimum generalized variance method, are proposed for estimating parameters in the correlation structure. We introduce a semiparametric varying coefficient partially linear model for longitudinal data and propose an estimation procedure for model coefficients by using a profile weighted least squares approach. Sampling properties of the proposed estimation procedures are studied and asymptotic normality of the resulting estimators is established. Finite sample performance of the proposed procedures is assessed by Monte Carlo simulation studies. The proposed methodology is illustrated by an analysis of a real data example.

 

 

 

 

 

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