Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/22636 
Year of Publication: 
2005
Series/Report no.: 
Technical Report No. 2005,22
Publisher: 
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
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.
Subjects: 
Compartmental Model
Model Discrimination
Discrimination Design
Locally Optimal Design
Robust Optimal Design
Maximin Optimal Design
Document Type: 
Working Paper

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