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dc.contributor.authorLee, JCen_US
dc.contributor.authorHsu, YLen_US
dc.date.accessioned2014-12-08T15:41:26Z-
dc.date.available2014-12-08T15:41:26Z-
dc.date.issued2003en_US
dc.identifier.issn0323-3847en_US
dc.identifier.urihttp://hdl.handle.net/11536/28192-
dc.identifier.urihttp://dx.doi.org/10.1002/bimj.200390003en_US
dc.description.abstractIn this paper we consider maximum likelihood analysis of generalized growth curve model with the Box-Cox transformation when the covariance matrix has AR(q) dependence structure with grouping variances. The covariance matrix under consideration is Sigma = DsigmaCDsigma where C is the correlation matrix with stationary autoregression process of order q, q < p and D-&sigma; is a diagonal matrix with p elements divided into g(&LE; p) groups, i.e., D-&sigma; is a function of {&sigma;(1),..., &sigma;(g)} and -1 < p < 1 and &sigma;(l), l = 1,...g, are unknown. We consider both parameter estimation and prediction of future values. Results are illustrated with real and simulated data.en_US
dc.language.isoen_USen_US
dc.subjectBox-Cox transformationen_US
dc.subjectheteroscedasticityen_US
dc.subjectmaximum likelihood estimatesen_US
dc.subjectpredictionsen_US
dc.subjectsimulationsen_US
dc.titleEstimation and prediction of generalized growth curve with grouping variances in AR(q) dependence structureen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/bimj.200390003en_US
dc.identifier.journalBIOMETRICAL JOURNALen_US
dc.citation.volume45en_US
dc.citation.issue2en_US
dc.citation.spage165en_US
dc.citation.epage181en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000181948400004-
dc.citation.woscount1-
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