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April 2013 Optimal discriminating designs for several competing regression models
Dietrich Braess, Holger Dette
Ann. Statist. 41(2): 897-922 (April 2013). DOI: 10.1214/13-AOS1103


The problem of constructing optimal discriminating designs for a class of regression models is considered. We investigate a version of the $T_{p}$-optimality criterion as introduced by Atkinson and Fedorov [Biometrika 62 (1975a) 289–303]. The numerical construction of optimal designs is very hard and challenging, if the number of pairwise comparisons is larger than 2. It is demonstrated that optimal designs with respect to this type of criteria can be obtained by solving (nonlinear) vector-valued approximation problems. We use a characterization of the best approximations to develop an efficient algorithm for the determination of the optimal discriminating designs. The new procedure is compared with the currently available methods in several numerical examples, and we demonstrate that the new method can find optimal discriminating designs in situations where the currently available procedures fail.


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Dietrich Braess. Holger Dette. "Optimal discriminating designs for several competing regression models." Ann. Statist. 41 (2) 897 - 922, April 2013.


Published: April 2013
First available in Project Euclid: 29 May 2013

zbMATH: 1360.62412
MathSciNet: MR3099125
Digital Object Identifier: 10.1214/13-AOS1103

Primary: 62K05
Secondary: 41A30 , 41A50

Keywords: model discrimination , optimal design , vector-valued approximation

Rights: Copyright © 2013 Institute of Mathematical Statistics


Vol.41 • No. 2 • April 2013
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