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Longitudinal data analysis using the conditional empirical likelihood method
dc.contributor.authorHan, Peisongen_US
dc.contributor.authorSong, Peter X.‐k.en_US
dc.contributor.authorWang, Luen_US
dc.date.accessioned2014-09-03T16:51:23Z
dc.date.availableWITHHELD_13_MONTHSen_US
dc.date.available2014-09-03T16:51:23Z
dc.date.issued2014-09en_US
dc.identifier.citationHan, Peisong; Song, Peter X.‐k. ; Wang, Lu (2014). "Longitudinal data analysis using the conditional empirical likelihood method." Canadian Journal of Statistics 42(3): 404-422.en_US
dc.identifier.issn0319-5724en_US
dc.identifier.issn1708-945Xen_US
dc.identifier.urihttps://hdl.handle.net/2027.42/108267
dc.description.abstractThis paper studies a new approach to longitudinal data analysis using the conditional empirical likelihood (CEL) method within the framework of marginal models. The possible unbalanced follow‐up visits are dealt with via stratification according to distinctive follow‐up patterns. The CEL method does not require any explicit modelling of the variance–covariance of the longitudinal outcomes. Instead, it implicitly incorporates a consistently estimated variance–covariance matrix in a nonparametric fashion. The proposed CEL estimator is connected to the generalized estimating equations (GEE) estimator, and achieves the same efficiency as the GEE estimator employing the true variance–covariance. The asymptotic distribution of the CEL estimator is derived, and simulation studies are conducted to assess the finite sample performance. Data collected from a longitudinal nutrition study are analysed as an application. The Canadian Journal of Statistics 42: 404–422; 2014 © 2014 Statistical Society of Canada Résumé Les auteurs proposent une nouvelle approche pour l'analyse de données longitudinales à l'aide de la méthode de la vraisemblance empirique conditionnelle (VEC) dans le cadre de modèles marginaux. Ils prennent en compte la possibilité d'un suivi irrégulier en stratifiant selon les séquences de suivis observées. La VEC ne nécessite pas la modélisation explicite de la variance‐covariance des résultats longitudinaux, mais en intègre plutôt implicitement un estimateur non paramétrique convergent. La VEC est associée aux équations d'estimation généralisées (EEG), et les estimateurs découlant de la VEC atteignent la même efficacité que ceux des EEG basées sur la vraie structure de variance‐covariance. Les auteurs présentent la distribution asymptotique de l'estimateur de la VEC, ainsi qu'une étude de simulation afin d’évaluer la performance de la méthode sur des échantillons finis. Ils effectuent finalement l'analyse des données d'une étude longitudinale portant sur la nutrition. La revue canadienne de statistique 42: 404–422; 2014 © 2014 Société statistique du Canadaen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherMarginal Modelen_US
dc.subject.otherGeneralized Estimating Equations (GEE)en_US
dc.subject.otherUnbalanced Longitudinal Dataen_US
dc.subject.otherVariance–Covariance Matrixen_US
dc.subject.otherWithin‐Subject Correlationen_US
dc.subject.otherMSC 2010 : Primary 62F12en_US
dc.subject.otherSecondary 62J12en_US
dc.titleLongitudinal data analysis using the conditional empirical likelihood methoden_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/108267/1/cjs11221-sm-0001-SupInfo-S1.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/108267/2/cjs11221.pdf
dc.identifier.doi10.1002/cjs.11221en_US
dc.identifier.sourceCanadian Journal of Statisticsen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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