Optimal discrimination designs for semiparametric models

• 成果类型: Article
• 署名作者: Dette, H.; Guchenko, R.; Melas, V. B.; Wong, W. K.
• 署名单位: Ruhr University Bochum; Saint Petersburg State University; University of California System; University of California Los Angeles
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx058
• 发表日期: 2018
• 页码: 185197
• 关键词: parameter-estimation; regression-models; rival models; criterion

摘要

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 by Lpez-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.