Optimal discrimination designs for semi-parametric models

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dc.contributor.authorDette, Holger
dc.contributor.authorGuchenko, Roman
dc.contributor.authorMelas, Viatcheslav
dc.contributor.authorWong, Weng Kee
dc.date.accessioned2016-10-28T12:33:36Z
dc.date.available2016-10-28T12:33:36Z
dc.date.issued2016
dc.description.abstractMuch of the work in the literature on optimal discrimination designs assumes that the models of interest are fully specified, apart from unknown parameters in some models. Recent work allows errors in the models to be non-normally distributed but still requires the specification of the mean structures. This research is motivated by the interesting work of Otsu (2008) to discriminate among semi-parametric models by generalizing the KL-optimality criterion proposed by Lopez-Fidalgo et al. (2007) and Tommasi and Lopez-Fidalgo (2010). In our work we provide further important insights in this interesting optimality criterion. In particular, we propose a practical strategy for finding optimal discrimination designs among semi-parametric models that can also be verified using an equivalence theorem. In addition, we study properties of such optimal designs and identify important cases where the proposed semi-parametric optimal discrimination designs coincide with the celebrated T-optimal designs.en
dc.identifier.urihttp://hdl.handle.net/2003/35306
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17349
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;59, 2016en
dc.subjectcontinuous designen
dc.subjectvariational calculusen
dc.subjectT-optimalityen
dc.subjectsemiparametric modelen
dc.subjectequivalence theoremen
dc.subjectdiscrimination designen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleOptimal discrimination designs for semi-parametric modelsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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