Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1541-0420.2005.00321.x
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dc.titleEffects of variance-function misspecification in analysis of longitudinal data
dc.contributor.authorWang, Y.-G.
dc.contributor.authorLin, X.
dc.date.accessioned2014-10-28T05:11:38Z
dc.date.available2014-10-28T05:11:38Z
dc.date.issued2005-06
dc.identifier.citationWang, Y.-G., Lin, X. (2005-06). Effects of variance-function misspecification in analysis of longitudinal data. Biometrics 61 (2) : 413-421+649. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1541-0420.2005.00321.x
dc.identifier.issn0006341X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105106
dc.description.abstractThe approach of generalized estimating equations (GEE) is based on the framework of generalized linear models but allows for specification of a working matrix for modeling within-subject correlations. The variance is often assumed to be a known function of the mean. This article investigates the impacts of misspecifying the variance function on estimators of the mean parameters for quantitative responses. Our numerical studies indicate that (1) correct specification of the variance function can improve the estimation efficiency even if the correlation structure is misspecified; (2) misspecification of the variance function impacts much more on estimators for within-cluster covariates than for cluster-level covariates; and (3) if the variance function is misspecified, correct choice of the correlation structure may not necessarily improve estimation efficiency. We illustrate impacts of different variance functions using a real data set from cow growth.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1111/j.1541-0420.2005.00321.x
dc.sourceScopus
dc.subjectAsymptotic efficiency
dc.subjectCorrelated data
dc.subjectEstimating functions
dc.subjectGaussian estimation
dc.subjectLongitudinal data
dc.subjectMisspecification
dc.subjectPseudolikelihood
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1111/j.1541-0420.2005.00321.x
dc.description.sourcetitleBiometrics
dc.description.volume61
dc.description.issue2
dc.description.page413-421+649
dc.description.codenBIOMA
dc.identifier.isiut000229893900010
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