OPTIMAL DISCRIMINATING DESIGNS FOR SEVERAL COMPETING REGRESSION MODELS
成果类型：
Article
署名作者：
Braess, Dietrich; Dette, Holger
署名单位：
Ruhr University Bochum
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/13-AOS1103
发表日期：
2013
页码：
897-922
关键词：
摘要：
The problem of constructing optimal discriminating designs for a class of regression models is considered. We investigate a version of the optimality criterion 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.