On the number of support points of maximin and Bayesian D-optimal designs in nonlinear regression models
| dc.contributor.author | Braess, Dietrich | de |
| dc.contributor.author | Dette, Holger | de |
| dc.date.accessioned | 2005-01-31T08:15:38Z | |
| dc.date.available | 2005-01-31T08:15:38Z | |
| dc.date.issued | 2004 | de |
| dc.description.abstract | We consider maximin and Bayesian D -optimal designs for nonlinear regression models. The maximin criterion requires the specification of a region for the nonlinear parameters in the model, while the Bayesian optimality criterion assumes that a prior distribution for these parameters is available. It was observed empirically by many authors that an increase of uncertainty in the prior information (i.e. a larger range for the parameter space in the maximin criterion or a larger variance of the prior distribution in the Bayesian criterion) yields a larger number of support points of the corresponding optimal designs. In this paper we present a rigorous proof of this phenomenon and show that in many nonlinear regression models the number of support points of Bayesian- and maximin D -optimal designs can become arbitrarily large if less prior information is available. Our results also explain why maximin D -optimal designs are usually supported at more different points than Bayesian D -optimal designs. | de |
| dc.format.extent | 191797 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/2003/20092 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-2766 | |
| dc.language.iso | en | de |
| dc.publisher | Universität Dortmund | de |
| dc.subject.ddc | 310 | de |
| dc.title | On the number of support points of maximin and Bayesian D-optimal designs in nonlinear regression models | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | en |
| dcterms.accessRights | open access |
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