Optimal discrimination designs for semi-parametric models
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Date
2016
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Abstract
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 Lopez-Fidalgo et al. (2007) and Tommasi and
Lopez-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.
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Keywords
continuous design, variational calculus, T-optimality, semiparametric model, equivalence theorem, discrimination design