  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.
