Optimal discrimination designs for exponential regression models
Biedermann, Stefanie, Dette, Holger and Pepelyshev, Andrey (2007) Optimal discrimination designs for exponential regression models. Journal of Statistical Planning and Inference, 137, (8), 2579-2592. (doi:10.1016/j.jspi.2006.03.015).
This is the latest version of this item.
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.
|Keywords:||compartmental model, model discrimination, discrimination design, locally optimal design, robust optimal design, maximin optimal design|
|Subjects:||Q Science > QA Mathematics
Q Science > QH Natural history > QH301 Biology
H Social Sciences > HA Statistics
|Divisions:||University Structure - Pre August 2011 > School of Mathematics > Statistics
|Date Deposited:||19 Aug 2008|
|Last Modified:||08 Jun 2012 12:24|
|Contributors:||Biedermann, Stefanie (Author)
Dette, Holger (Author)
Pepelyshev, Andrey (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Available Versions of this Item
Optimal discrimination designs for exponential regression models. (deposited 23 Apr 2007)
- Optimal discrimination designs for exponential regression models. (deposited 19 Aug 2008) [Currently Displayed]
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