Efficient experimental designs for sigmoidal growth models

dc.contributor.author	Dette, Holger	de
dc.contributor.author	Pepelyshev, Andrey	de
dc.date.accessioned	2005-05-31T10:57:18Z
dc.date.available	2005-05-31T10:57:18Z
dc.date.issued	2005	de
dc.description.abstract	For the Weibull- and Richards-regression model robust designs are determined by maximizing a minimum of D- or D1-efficiencies, taken over a certain range of the non-linear parameters. It is demonstrated that the derived designs yield a satisfactory solution of the optimal design problem for this type of model in the sense that these designs are efficient and robust with respect to misspecification of the unknown parameters. Moreover, the designs can also be used for testing the postulated form of the regression model against a simplified sub-model.	en
dc.format.extent	198209 bytes
dc.format.extent	363409 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/21353
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-6731
dc.language.iso	de	de
dc.publisher	Universität Dortmund	de
dc.subject	Sigmoidal growth	en
dc.subject	Weibull regression model	en
dc.subject	exponential regression model	en
dc.subject	Richards-regression model	en
dc.subject	logistic regression model	en
dc.subject	robust optimal design	en
dc.subject	goodness-of-fit test	en
dc.subject.ddc	310	de
dc.title	Efficient experimental designs for sigmoidal growth models	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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