Optimal discrimination designs for semi-parametric models
  Much of the work in the literature on optimal discrimination designs assumes that the models of interest are fully specified, apart from unknown parameters in some models. Recent work allows errors in the models to be non-normally distributed but still requires the specification of the mean structures. This research is motivated by the interesting work of Otsu (2008) to discriminate among semi-parametric models by generalizing the KL-optimality criterion proposed by L\'opez-Fidalgo et al. (2007) and Tommasi and L\'opez-Fidalgo (2010). In our work we provide further important insights in this interesting optimality criterion. In particular, we propose a practical strategy for finding optimal discrimination designs among semi-parametric models that can also be verified using an equivalence theorem. In addition, we study properties of such optimal designs and identify important cases where the proposed semi-parametric optimal discrimination designs coincide with the celebrated T -optimal designs. 
Holger Dette, Roman Guchenko, Viatcheslav Melas, Weng Kee Wong
stat.ME math.ST stat.TH