Robust T-optimal discriminating designs
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Date
2012-07-05
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Abstract
This paper considers the problem of constructing optimal discriminating experimental
designs for competing regression models on the basis of the T-optimality criterion introduced
by Atkinson and Fedorov (1975a). T-optimal designs depend on unknown model
parameters and it is demonstrated that these designs are sensitive with respect to misspecification. As a solution of this problem we propose a Bayesian and standardized maximin
approach to construct robust and efficient discriminating designs on the basis of the T-
optimality criterion. It is shown that the corresponding Bayesian and standardized maximin
optimality criteria are closely related to linear optimality criteria. For the problem
of discriminating between two polynomial regression models which differ in the degree by
two the robust T-optimal discriminating designs can be found explicitly. The results are
illustrated in several examples.
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Keywords
Chebyshev polynomial, linear optimality criteria, model discrimination, optimal design, robust design