Locally optimal designs for errors-in-variables models

dc.contributor.author	Konstantinou, Maria
dc.contributor.author	Dette, Holger
dc.date.accessioned	2014-09-05T11:39:31Z
dc.date.available	2014-09-05T11:39:31Z
dc.date.issued	2014-09-05
dc.description.abstract	This paper considers the construction of optimal designs for nonlinear regres- sion models when there are measurement errors in the predictor. Corresponding (approximate) design theory is developed for maximum likelihood and least squares estimation, where the latter leads to non-concave optimisation problems. For the Michaelis-Menten, EMAX and exponential regression model D-optimal designs can be found explicitly and compared with the corresponding designs derived under the assumption of no measurement error in concrete applications.	en
dc.identifier.uri	http://hdl.handle.net/2003/33611
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-6876
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB 823;31/2014	en
dc.subject	error-in-variable model	en
dc.subject	D-optimality	en
dc.subject	nonlinear regression	en
dc.subject	optimal design	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Locally optimal designs for errors-in-variables models	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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