Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/49339 
Kompletter Metadatensatz
DublinCore-FeldWertSprache
dc.contributor.authorDette, Holgeren
dc.contributor.authorHaines, Linda M.en
dc.contributor.authorImhof, Lorens A.en
dc.date.accessioned2011-09-06T11:41:33Z-
dc.date.available2011-09-06T11:41:33Z-
dc.date.issued2003-
dc.identifier.urihttp://hdl.handle.net/10419/49339-
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
dc.language.isoengen
dc.publisher|aUniversität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen |cDortmunden
dc.relation.ispartofseries|aTechnical Report |x2003,10en
dc.subject.ddc519en
dc.subject.keywordmaximin optimal designsen
dc.subject.keywordBayesian optimal designsen
dc.subject.keywordnonlinear regression modelsen
dc.subject.keywordparameter estimationen
dc.subject.keywordleast favourable prioren
dc.subject.stwRegressionen
dc.subject.stwSchätztheorieen
dc.subject.stwTheorieen
dc.titleMaximin and Bayesian optimal designs for regression models-
dc.typeWorking Paperen
dc.identifier.ppn818003502en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:sfb475:200310en

Datei(en):
Datei
Größe
164.75 kB
328.02 kB





Publikationen in EconStor sind urheberrechtlich geschützt.