Resource title

Optimal Discrimination Designs for Exponential Regression Models

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Resource description

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.

Resource author

Stefanie Biedermann, Holger Dette, Andrey Pepelyshev

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Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/22636

Resource license

Adapt according to the presented license agreement and reference the original author.