Full metadata record
DC FieldValueLanguage
dc.contributor.authorKonstantinou, Maria-
dc.contributor.authorDette, Holger-
dc.date.accessioned2016-05-18T10:31:47Z-
dc.date.available2016-05-18T10:31:47Z-
dc.date.issued2016-
dc.identifier.urihttp://hdl.handle.net/2003/34966-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17014-
dc.description.abstractBayesian optimality criteria provide a robust design strategy to parameter misspeci- fication. We develop an approximate design theory for Bayesian D-optimality for non- linear regression models with covariates subject to measurement errors. Both maximum likelihood and least squares estimation are studied and explicit characterisations of the Bayesian D-optimal saturated designs for the Michaelis-Menten, Emax and exponential regression models are provided. Several data examples are considered for the case of no preference for specific parameter values, where Bayesian D-optimal saturated designs are calculated using the uniform prior and compared to several other designs, including the corresponding locally D-optimal designs, which are often used in practice.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;24, 2016en
dc.subjecterror-in-variables modelsen
dc.subjectD-optimalityen
dc.subjectBayesian optimal designsen
dc.subjectclassical errorsen
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleBayesian D-optimal designs for error-in-variables modelsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 823

Files in This Item:
File Description SizeFormat 
DP_2416_SFB823_Konstantinou_Dette.pdfDNB310.72 kBAdobe PDFView/Open


This item is protected by original copyright



This item is protected by original copyright rightsstatements.org