Authors: Braess, Dietrich
Dette, Holger
Title: On the number of support points of maximin and Bayesian D-optimal designs in nonlinear regression models
Language (ISO): en
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.
URI: http://hdl.handle.net/2003/20092
http://dx.doi.org/10.17877/DE290R-2766
Issue Date: 2004
Publisher: Universität Dortmund
Appears in Collections:Sonderforschungsbereich (SFB) 475

Files in This Item:
File Description SizeFormat 
78_04.pdfDNB187.3 kBAdobe PDFView/Open


This item is protected by original copyright



All resources in the repository are protected by copyright.