Robust T-optimal discriminating designs

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dc.contributor.authorDette, Holger
dc.contributor.authorMelas, Viatcheslav B.
dc.contributor.authorShpilev, Petr
dc.date.accessioned2012-07-05T12:10:04Z
dc.date.available2012-07-05T12:10:04Z
dc.date.issued2012-07-05
dc.description.abstractThis paper considers the problem of constructing optimal discriminating experimental designs for competing regression models on the basis of the T-optimality criterion introduced by Atkinson and Fedorov (1975a). T-optimal designs depend on unknown model parameters and it is demonstrated that these designs are sensitive with respect to misspecification. As a solution of this problem we propose a Bayesian and standardized maximin approach to construct robust and efficient discriminating designs on the basis of the T- optimality criterion. It is shown that the corresponding Bayesian and standardized maximin optimality criteria are closely related to linear optimality criteria. For the problem of discriminating between two polynomial regression models which differ in the degree by two the robust T-optimal discriminating designs can be found explicitly. The results are illustrated in several examples.en
dc.identifier.urihttp://hdl.handle.net/2003/29498
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14338
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;25/2012en
dc.subjectChebyshev polynomialen
dc.subjectlinear optimality criteriaen
dc.subjectmodel discriminationen
dc.subjectoptimal designen
dc.subjectrobust designen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleRobust T-optimal discriminating designsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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Sonderforschungsbereich (SFB) 823