T-optimal discriminating designs for Fourier regression models
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Date
2015
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Abstract
In this paper we consider the problem of constructing T-optimal discriminating designs
for Fourier regression models. We provide explicit solutions of the optimal design problem for
discriminating between two Fourier regression models, which differ by at most three trigonometric
functions. In general, the T-optimal discriminating design depends in a complicated
way on the parameters of the larger model, and for special configurations of the parameters
T-optimal discriminating designs can be found analytically. Moreover, we also study this
dependence in the remaining cases by calculating the optimal designs numerically. In particular,
it is demonstrated that D- and Ds-optimal designs have rather low efficiencies with
respect to the T-optimality criterion.
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Keywords
T-optimal design, trigonometric models, Chebyshev polynomial, linear optimality criteria, model discrimination