Robust discrimination designs
We study the construction of experimental designs, the purpose of which is to aid in the discrimination between two possibly non-linear regression models, each of which might be only approximately specified. A rough description of our approach is that we impose neighbourhood structures on each regression response and determine the members of these neighbourhoods which are least favourable in the sense of minimizing the Kullback–Leibler divergence. Designs are obtained which maximize this minimum divergence. Both static and sequential approaches are studied. We then consider sequential designs whose purpose is initially to discriminate, but which move their emphasis towards efficient estimation or prediction as one model becomes favoured over the other.
Keywords: Discrimination; D–T-optimality; Integrated Kullback–Leibler optimality; Kullback– Leibler; Kullback–Leibler optimality; Maximin; Michaelis–Menten model; Neyman–Pearson test; Non-linear regression; Robustness; Sequential designs; Simulated annealing; T-optimal design
Document Type: Research Article
Affiliations: University of Alberta, Edmonton, Canada
Publication date: 2009-09-01