Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection

Wang, You-Gan & Hin, Lin-Yee (2010) Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection. Computational Statistics & Data Analysis, 54(12), pp. 3359-3370.

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Abstract

A modeling paradigm is proposed for covariate, variance and working correlation structure selection for longitudinal data analysis. Appropriate selection of covariates is pertinent to correct variance modeling and selecting the appropriate covariates and variance function is vital to correlation structure selection. This leads to a stepwise model selection procedure that deploys a combination of different model selection criteria. Although these criteria find a common theoretical root based on approximating the Kullback-Leibler distance, they are designed to address different aspects of model selection and have different merits and limitations. For example, the extended quasi-likelihood information criterion (EQIC) with a covariance penalty performs well for covariate selection even when the working variance function is misspecified, but EQIC contains little information on correlation structures. The proposed model selection strategies are outlined and a Monte Carlo assessment of their finite sample properties is reported. Two longitudinal studies are used for illustration.

Impact and interest:

4 citations in Scopus
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4 citations in Web of Science®

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ID Code: 90446
Item Type: Journal Article
Refereed: Yes
Keywords: generalized estimating equations, working-correlation-structure, quasi-likelihood, linear-models, gee analyses, misspecification, criteria, error
DOI: 10.1016/j.csda.2009.11.006
ISSN: 0167-9473
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 17 Nov 2015 03:20
Last Modified: 03 Dec 2015 05:48

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