Optimal discrimination designs for semiparametric models

Dette, H. and Guchenko, R. and Melas, V.B. and Wong, W.K.

Volume: 105 Pages: 185-197
DOI: 10.1093/biomet/asx058
Published: 2018

Much work on optimal discrimination designs assumes that the models of interest are fully specified, apart from unknown parameters. Recent work allows errors in the models to be nonnormally distributed but still requires the specification of the mean structures.Otsu (2008) proposed optimal discriminating designs for semiparametric models by generalizing the Kullback-Leibler optimality criterion proposed byLópez-Fidalgo et al. (2007). This paper develops a relatively simple strategy for finding an optimal discrimination design. We also formulate equivalence theorems to confirm optimality of a design and derive relations between optimal designs found here for discriminating semiparametric models and those commonly used in optimal discrimination design problems. © 2017 Biometrika Trust.

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