Bayesian T-optimal discriminating designs

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dc.contributor.authorDette, Holger
dc.contributor.authorMelas, Viatcheslav B.
dc.contributor.authorGuchenko, Roman
dc.date.accessioned2014-12-10T12:59:41Z
dc.date.available2014-12-10T12:59:41Z
dc.date.issued2014
dc.description.abstractThe problem of constructing Bayesian optimal discriminating designs for a class of regression models with respect to the T-optimality criterion introduced by Atkinson and Fedorov (1975a) is considered. It is demonstrated that the discretization of the integral with respect to the prior distribution leads to locally T-optimal discriminating design problems with a large number of model comparisons. Current methodology for the numerical construction of discrimination designs can only deal with a few comparisons, but the discretization of the Bayesian prior easily yields to discrimination design problems for more than 100 competing models. A new efficient method is developed to deal with problems of this type. It combines some features of the classical exchange type algorithm with the gradient methods. Convergence is proved and it is demonstrated that the new method can find Bayesian optimal discriminating designs in situations where all currently available procedures fail.en
dc.identifier.urihttp://hdl.handle.net/2003/33768
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-6712
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;40/2014en
dc.subjectdesign of experimenten
dc.subjectmodel uncertaintyen
dc.subjectgradient methodsen
dc.subjectmodel discriminationen
dc.subjectBayesian optimal designen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleBayesian T-optimal discriminating designsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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