Bayesian and Maximum Optimal Designs for Heteroscedastic Regression Models

dc.contributor.author	Dette, Holger	de
dc.contributor.author	Haines, Linda M.	de
dc.contributor.author	Inhof, Lorens A.	de
dc.date.accessioned	2004-12-06T18:41:32Z
dc.date.available	2004-12-06T18:41:32Z
dc.date.issued	2003	de
dc.description.abstract	The problem of constructing standardized maximin D-optimal designs for weighted polynomial regression models is addressed. In particular it is shown that, by following the broad approach to the construction of maximin designs introduced recently by Dette, Haines and Imhof (2003), such designs can be obtained as weak limits of the corresponding Bayesian Φ_q-optimal designs. The approach is illustrated for two specific weighted polynomial models and also for a particular growth model.	en
dc.format.extent	214275 bytes
dc.format.extent	394243 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4998
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-2697
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	Bayesian design	en
dc.subject	D-optimal design	en
dc.subject	maximin design	en
dc.subject	polynomial regression	en
dc.subject	standardized criterion	en
dc.subject.ddc	310	de
dc.title	Bayesian and Maximum Optimal Designs for Heteroscedastic Regression Models	de
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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