Some Robust Design Strategies for Percentile Estimation in Binary Response Models

dc.contributor.author	Biedermann, Stefanie	de
dc.contributor.author	Dette, Holger	de
dc.contributor.author	Pepelyshev, Andrey	de
dc.date.accessioned	2004-12-06T18:39:07Z
dc.date.available	2004-12-06T18:39:07Z
dc.date.issued	2004	de
dc.format.extent	191298 bytes
dc.format.extent	353076 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4890
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-16156
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	For the problem of percentile estimation of a quantal response curve, we determine multi-objective designs which are robust with respect to misspecifications of the model assumptions. We propose a maximin approach based on efficiencies and provide designs that are simultaneously efficient with respect to the particular choice of various parameter regions and link functions. Furthermore, we deal with the problems of designing model and percentile robust experiments and give various examples of such designs, which are calculated numerically.	en
dc.subject	binary response model	en
dc.subject	robust optimal design	en
dc.subject	c-efficiency	en
dc.subject	percentile estimation	en
dc.subject	multi-objective designs	en
dc.subject.ddc	310	de
dc.title	Some Robust Design Strategies for Percentile Estimation in Binary Response Models	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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