Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13878
Title: Efficient estimation for covariance parameters in analysis of longitudinal data
Authors: ZHAO YUNING
Keywords: Efficiency; Gaussian approach; Generalized estimation equations; Longitudinal data; Variance function; Working correlation
Issue Date: 18-Mar-2004
Source: ZHAO YUNING (2004-03-18). Efficient estimation for covariance parameters in analysis of longitudinal data. ScholarBank@NUS Repository.
Abstract: In longitudinal data analysis, the Generalized estimation equation(GEE) approach is a milestone for estimation of regression parameters. Much theoretic work has been done in the literature and the GEE is also found to be a convenient tool for real data analysis. However, the choice of ``working'' covariance structures in the GEE approach affects the estimation efficiency greatly. In most cases, we only focus on the specification in correlation structures and neglect the importance of specification in variance functions. In this thesis, the variance function will be estimated instead of being assumed to be known, and the effects of the variance parameters estimates on estimation of regression parameters are considered. The Gaussian method is proposed to estimate the variance parameters because it can provide consistent estimation even without any information of correlation structures. Quasi-likelihood and weighted least square estimation methods are also introduced. Simulation studies are carried out to verify the analytical results. We also illustrate our findings by analyzing the well known epileptic dataset.
URI: http://scholarbank.nus.edu.sg/handle/10635/13878
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ZhaoYN.pdf849.5 kBAdobe PDF

OPEN

NoneView/Download

Page view(s)

208
checked on Dec 11, 2017

Download(s)

142
checked on Dec 11, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.