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Estimation and prediction in linear mixed models with skew-normal random effects for longitudinal data

dc.citation.epage1507en_US
dc.citation.issue9en_US
dc.citation.spage1490en_US
dc.citation.volume27en_US
dc.citation.woscount28
dc.contributor.authorLin, Tsung I.en_US
dc.contributor.authorLee, Jack C.en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.date.accessioned2014-12-08T15:12:17Z
dc.date.available2014-12-08T15:12:17Z
dc.date.issued2008-04-30en_US
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.identifier.doi10.1002/sim.3026en_US
dc.identifier.issn0277-6715en_US
dc.identifier.journalSTATISTICS IN MEDICINEen_US
dc.identifier.urihttp://dx.doi.org/10.1002/sim.3026en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/9438
dc.identifier.wosnumberWOS:000255885700010
dc.language.isoen_USen_US
dc.subjectECME algorithmen_US
dc.subjectmaximum-likelihood estimationen_US
dc.subjectpredictionen_US
dc.subjectrandom effectsen_US
dc.subjectSNLMMen_US
dc.titleEstimation and prediction in linear mixed models with skew-normal random effects for longitudinal dataen_US
dc.typeArticleen_US

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