Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37092
DC FieldValueLanguage
dc.contributor.authorLin, T.I.en_US
dc.contributor.author林宗儀zh_TW
dc.contributor.authorLee, J.C.en_US
dc.date2008zh_TW
dc.date.accessioned2014-06-06T07:58:38Z-
dc.date.available2014-06-06T07:58:38Z-
dc.identifier.issn0277-6715zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/37092-
dc.description.abstractThis paper extends the classical linear mixed model by considering a multivariate skew-normal assumption for the distribution of random effects. We present an efficient hybrid ECME-NR algorithm for the computation of maximum-likelihood estimates of parameters. A score test statistic for testing the existence of skewness preference among random effects is developed. The technique for the prediction of future responses under this model is also investigated. The methodology is illustrated through an application to Framingham cholesterol data and a simulation study. Copyright (C) 2007 John Wiley & Sons, Ltd.en_US
dc.language.isoen_USzh_TW
dc.relationStatistics in Medicineen_US
dc.relation.ispartofseriesStatistics in Medicine, Volume 27, Issue 9, Page(s) 1490-1507.en_US
dc.relation.urihttp://dx.doi.org/10.1002/sim.3026en_US
dc.subjectECME algorithmen_US
dc.subjectmaximum-likelihood estimationen_US
dc.subjectpredictionen_US
dc.subjectrandomen_US
dc.subjecteffectsen_US
dc.subjectSNLMMen_US
dc.subjectmultivariate-t-distributionen_US
dc.subjectmaximum-likelihooden_US
dc.subjectbayesian-analysisen_US
dc.subjectdistributionsen_US
dc.subjectalgorithmen_US
dc.subjectextensionen_US
dc.subjectfamiliesen_US
dc.subjectecmen_US
dc.subjectemen_US
dc.titleEstimation and prediction in linear mixed models with skew-normal random effects for longitudinal dataen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1002/sim.3026zh_TW
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