Optimal discrimination designs for exponential regression models
Optimal discrimination designs for exponential regression models
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.
compartmental model, model discrimination, discrimination design, locally optimal design, robust optimal design, maximin optimal design
2579-2592
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Dette, Holger
8c7b1c2e-3adc-45df-acfc-9e76509a228e
Pepelyshev, Andrey
136925bf-d2eb-4d4f-998f-48218155ce62
2007
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Dette, Holger
8c7b1c2e-3adc-45df-acfc-9e76509a228e
Pepelyshev, Andrey
136925bf-d2eb-4d4f-998f-48218155ce62
Biedermann, Stefanie, Dette, Holger and Pepelyshev, Andrey
(2007)
Optimal discrimination designs for exponential regression models.
Journal of Statistical Planning and Inference, 137 (8), .
(doi:10.1016/j.jspi.2006.03.015).
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.
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Published date: 2007
Keywords:
compartmental model, model discrimination, discrimination design, locally optimal design, robust optimal design, maximin optimal design
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Statistics
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Local EPrints ID: 58645
URI: http://eprints.soton.ac.uk/id/eprint/58645
ISSN: 0378-3758
PURE UUID: 6d01a34f-a5c3-4c6a-a4f6-585a093785d0
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Date deposited: 19 Aug 2008
Last modified: 16 Mar 2024 03:51
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Author:
Holger Dette
Author:
Andrey Pepelyshev
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