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Optimal discrimination designs for semiparametric models. / Мелас, Вячеслав Борисович; Гученко, Роман Александрович; Dette, Holger; Wong, Weng Kee.

в: Biometrika, Том 105, № 1, 03.2018, стр. 185-197.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Мелас, ВБ, Гученко, РА, Dette, H & Wong, WK 2018, 'Optimal discrimination designs for semiparametric models', Biometrika, Том. 105, № 1, стр. 185-197. https://doi.org/10.1093/biomet/asx058

APA

Мелас, В. Б., Гученко, Р. А., Dette, H., & Wong, W. K. (2018). Optimal discrimination designs for semiparametric models. Biometrika, 105(1), 185-197. https://doi.org/10.1093/biomet/asx058

Vancouver

Мелас ВБ, Гученко РА, Dette H, Wong WK. Optimal discrimination designs for semiparametric models. Biometrika. 2018 Март;105(1):185-197. https://doi.org/10.1093/biomet/asx058

Author

Мелас, Вячеслав Борисович ; Гученко, Роман Александрович ; Dette, Holger ; Wong, Weng Kee. / Optimal discrimination designs for semiparametric models. в: Biometrika. 2018 ; Том 105, № 1. стр. 185-197.

BibTeX

@article{aa871f4343f14c1fae733b9eef1081af,
title = "Optimal discrimination designs for semiparametric models",
abstract = "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 byL{\'o}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.",
keywords = "Continuous design, Equivalence theorem, Kullback-Leibler divergence, T-optimality, Variational calculus, PARAMETER-ESTIMATION, REGRESSION-MODELS, RIVAL MODELS, CRITERION",
author = "Мелас, {Вячеслав Борисович} and Гученко, {Роман Александрович} and Holger Dette and Wong, {Weng Kee}",
note = "Funding Information: We are grateful to the reviewers for their constructive comments on the first version of our paper. Dette and Guchenko were supported by the Deutsche Forschungsgemeinschaft. Dette and Wong were partially supported by the National Institute of General Medical Sciences of the U.S. National Institutes of Health. Melas and Guchenko were partially supported by St. Petersburg State University and the Russian Foundation for Basic Research.",
year = "2018",
month = mar,
doi = "10.1093/biomet/asx058",
language = "English",
volume = "105",
pages = "185--197",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",
number = "1",

}

RIS

TY - JOUR

T1 - Optimal discrimination designs for semiparametric models

AU - Мелас, Вячеслав Борисович

AU - Гученко, Роман Александрович

AU - Dette, Holger

AU - Wong, Weng Kee

N1 - Funding Information: We are grateful to the reviewers for their constructive comments on the first version of our paper. Dette and Guchenko were supported by the Deutsche Forschungsgemeinschaft. Dette and Wong were partially supported by the National Institute of General Medical Sciences of the U.S. National Institutes of Health. Melas and Guchenko were partially supported by St. Petersburg State University and the Russian Foundation for Basic Research.

PY - 2018/3

Y1 - 2018/3

N2 - 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.

AB - 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.

KW - Continuous design

KW - Equivalence theorem

KW - Kullback-Leibler divergence

KW - T-optimality

KW - Variational calculus

KW - PARAMETER-ESTIMATION

KW - REGRESSION-MODELS

KW - RIVAL MODELS

KW - CRITERION

UR - http://www.scopus.com/inward/record.url?scp=85043244627&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/optimal-discrimination-designs-semiparametric-models

U2 - 10.1093/biomet/asx058

DO - 10.1093/biomet/asx058

M3 - Article

VL - 105

SP - 185

EP - 197

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 1

ER -

ID: 35200489