Optimal discrimination designs for semiparametric models

被引：3

作者：

Dette, H. ^{[1
]}

Guchenko, R. ^{[2
]}

Melas, V. B. ^{[2
]}

Wong, W. K. ^{[3
]}

机构：

[1] Ruhr Univ Bochum, Fak Math, D-44780 Bochum, Germany

[2] St Petersburg State Univ, Fac Math & Mech, St Petersburg 198504, Russia

[3] Univ Calif Los Angeles, Dept Biostat, 650 Charles E Young Dr South, Los Angeles, CA 90095 USA

来源：

BIOMETRIKA | 2018年 / 105卷 / 01期

基金：

美国国家卫生研究院; 俄罗斯基础研究基金会;

关键词：

Continuous design; Equivalence theorem; Kullback-Leibler divergence; T-optimality; Variational calculus; PARAMETER-ESTIMATION; REGRESSION-MODELS; RIVAL MODELS; CRITERION;

D O I：

10.1093/biomet/asx058

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

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 L<remove>pez-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.

引用

页码：185 / 197

页数：13

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