Optimal designs for comparing regression models with correlated observations

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dc.contributor.authorDette, Holger
dc.contributor.authorSchorning, Kirsten
dc.contributor.authorKonstantinou, Maria
dc.date.accessioned2016-01-26T11:39:17Z
dc.date.available2016-01-26T11:39:17Z
dc.date.issued2016
dc.description.abstractWe consider the problem of efficient statistical inference for comparing two regression curves estimated from two samples of dependent measurements. Based on a representation of the best pair of linear unbiased estimators in continuous time models as a stochastic integral, an efficient pair of linear unbiased estimators with corresponding optimal designs for finite sample size is constructed. This pair minimises the width of the confidence band for the difference between the estimated curves. We thus extend results readily available in the literature to the case of correlated observations and provide an easily implementable and efficient solution. The advantages of using such pairs of estimators with corresponding optimal designs for the comparison of regression models are illustrated via numerical examples.en
dc.identifier.urihttp://hdl.handle.net/2003/34472
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-16528
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;5/2016en
dc.subjectlinear regressionen
dc.subjectoptimal designen
dc.subjectconfidence banden
dc.subjectcomparing regression curvesen
dc.subjectcorrelated observationsen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleOptimal designs for comparing regression models with correlated observationsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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