Finite sample performance of sequential designs for model identification

dc.contributor.authorDette, Holgerde
dc.contributor.authorKwiecien, Robertde
dc.date.accessioned2004-12-06T18:41:26Z
dc.date.available2004-12-06T18:41:26Z
dc.date.issued2003
dc.description.abstractClassical regression analysis is usually performed in two steps. In a first step an appropriate model is identified to describe the data generating process and in a second step statistical inference is performed in the identified model. An intuitively appealing approach to the design of experiment for these different purposes are sequential strategies, which use parts of the sample for model identification and adapt the design according to the outcome of the identification steps. In this paper we investigate the finite sample properties of two sequential design strategies, which were recently proposed in the literature. A detailed comparison of sequential designs for model discrimination in several regression models is given by means of a simulation study. Some non-sequential designs are also included in the study.en
dc.format.extent1238626 bytes
dc.format.extent17452935 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/4994
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14467
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectoptimal designen
dc.subjectrobust designen
dc.subjectdiscrimination designen
dc.subjectsequential designen
dc.subjectF-testen
dc.subject.ddc310de
dc.titleFinite sample performance of sequential designs for model identificationen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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