Authors: Biedermann, Stefanie
Dette, Holger
Title: Optimal discrimination designs for exponential regression models
Language (ISO): en
Abstract: We investigate optimal designs for discriminating between exponential regression models of different complexity, which are widely used in the biological sciences; see, e.g., Landaw (1995) or Gibaldi and Perrier (1982). We discuss different approaches for the construction of appropriate optimality criteria, and find sharper upper bounds on the number of support points of locally optimal discrimination designs than those given by Caratheodory’s Theorem. These results greatly facilitate the numerical construction of optimal designs. Various examples of optimal designs are then presented and compared to different other designs. Moreover, to protect the experiment against misspecifications of the nonlinear model parameters, we adapt the design criteria such that the resulting designs are robust with respect to such misspecifications and, again, provide several examples, which demonstrate the advantages of our approach.
Subject Headings: Compartmental model
Discrimination design
Locally optimal design
Maximin optimal design
Model discrimination
Robust optimal design
URI: http://hdl.handle.net/2003/21540
http://dx.doi.org/10.17877/DE290R-1954
Issue Date: 2005-07-29T09:23:40Z
Appears in Collections:Sonderforschungsbereich (SFB) 475

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