Skip to main content

Robust discrimination designs

Buy Article:

$51.00 plus tax (Refund Policy)


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.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

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

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more