Bayesian D-optimal designs for error-in-variables models

dc.contributor.author	Konstantinou, Maria
dc.contributor.author	Dette, Holger
dc.date.accessioned	2016-05-18T10:31:47Z
dc.date.available	2016-05-18T10:31:47Z
dc.date.issued	2016
dc.description.abstract	Bayesian 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.identifier.uri	http://hdl.handle.net/2003/34966
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-17014
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;24, 2016	en
dc.subject	error-in-variables models	en
dc.subject	D-optimality	en
dc.subject	Bayesian optimal designs	en
dc.subject	classical errors	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Bayesian D-optimal designs for error-in-variables models	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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