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dc.contributor.authorDette, Holgeren_US
dc.contributor.authorHaines, Linda M.en_US
dc.contributor.authorImhof, Lorens A.en_US
dc.description.abstractFor many problems of statistical inference in regression modelling, the Fisher information matrix depends on certain nuisance parameters which are unknown and which enter the model nonlinearly. A common strategy to deal with this problem within the context of design is to construct maximin optimal designs as those designs which maximize the minimum value of a real valued (standardized) function of the Fisher information matrix, where the minimum is taken over a specified range of the unknown parameters. The maximin criterion is not differentiable and the construction of the associated optimal designs is therefore difficult to achieve in practice. In the present paper the relationship between maximin optimal designs and a class of Bayesian optimal designs for which the associated criteria are differentiable is explored. In particular, a general methodology for determining maximin optimal designs is introduced based on the fact that in many cases these designs can be obtained as weak limits of appropriate Bayesian optimal designs.en_US
dc.publisherUniv., SFB 475 Dortmunden_US
dc.relation.ispartofseriesTechnical Report // Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2003,10en_US
dc.subject.keywordmaximin optimal designsen_US
dc.subject.keywordBayesian optimal designsen_US
dc.subject.keywordnonlinear regression modelsen_US
dc.subject.keywordparameter estimationen_US
dc.subject.keywordleast favourable prioren_US
dc.titleMaximin and Bayesian optimal designs for regression modelsen_US
dc.typeWorking Paperen_US
Appears in Collections:Technical Reports, SFB 475: Komplexitätsreduktion in multivariaten Datenstrukturen, TU Dortmund

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