Skip to main content
Open Access Publications from the University of California


UCLA Previously Published Works bannerUCLA

Optimal discrimination designs for semiparametric models


Much work on optimal discrimination designs assumes that the models of interest are fullyspecified, apart from unknown parameters. Recent work allows errors in the models to be nonnormallydistributed but still requires the specification of the mean structures. Otsu (2008) proposedoptimal discriminating designs for semiparametric models by generalizing the Kullback–Leibleroptimality criterion proposed by López-Fidalgo et al. (2007). This paper develops a relativelysimple strategy for finding an optimal discrimination design.We also formulate equivalence theoremsto confirm optimality of a design and derive relations between optimal designs found herefor discriminating semiparametric models and those commonly used in optimal discriminationdesign problems.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View