ライブラリ登録: Guest
Begell Digital Portal Begellデジタルライブラリー 電子書籍 ジャーナル 参考文献と会報 リサーチ集
International Journal for Uncertainty Quantification
インパクトファクター: 3.259 5年インパクトファクター: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN 印刷: 2152-5080
ISSN オンライン: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2015012623
pages 569-583

ROBUSTNESS OF WILKS' CONSERVATIVE ESTIMATE OF CONFIDENCE INTERVALS

Jan Peter Hessling
SP Technical Research Institute of Sweden, Measurement Technology, Box 857, SE-50115 Boras, Sweden
Jeffrey Uhlmann
University of Missouri−Columbia, Department of Computer Science, 201 EBW, Columbia, Missouri 65211, USA

要約

The striking generality and simplicity of Wilks' method has made it popular for quantifying modeling uncertainty. A conservative estimate of the confidence interval is obtained from a very limited set of randomly drawn model sample values, with probability set by the assigned so-called stability. In contrast, the reproducibility of the estimated limits, or robustness, is beyond our control as it is strongly dependent on the probability distribution of model results. The inherent combination of random sampling and faithful estimation in Wilks' approach is here shown to often result in poor robustness. The estimated confidence interval is consequently not a well-defined measure of modeling uncertainty. To remedy this deficiency, adjustments of Wilks' approach as well as alternative novel, effective but less known approaches based on deterministic sampling are suggested. For illustration, the robustness of Wilks' estimate for uniform and normal model distributions are compared.


Articles with similar content:

RECONSTRUCTION OF DOMAIN BOUNDARY AND CONDUCTIVITY IN ELECTRICAL IMPEDANCE TOMOGRAPHY USING THE APPROXIMATION ERROR APPROACH
International Journal for Uncertainty Quantification, Vol.1, 2011, issue 3
Ville Kolehmainen, Jari P. Kaipio, Antti Nissinen
ACCURATE NUMERICAL SOLUTION OF THE SPRAY EQUATION USING PARTICLE METHODS
Atomization and Sprays, Vol.16, 2006, issue 2
Shankar Subramaniam, G. M. Pai
ROBUST UNCERTAINTY QUANTIFICATION USING PRECONDITIONED LEAST-SQUARES POLYNOMIAL APPROXIMATIONS WITH l1-REGULARIZATION
International Journal for Uncertainty Quantification, Vol.6, 2016, issue 1
D. Lucor, A. Belme, Jan Van Langenhove
The Use of Fuzzy A Priori Information for Estimation of Regression Parameters
Journal of Automation and Information Sciences, Vol.35, 2003, issue 1
Arnold S. Korkhin, Victor N. Mizernyi
ORTHOGONAL BASES FOR POLYNOMIAL REGRESSION WITH DERIVATIVE INFORMATION IN UNCERTAINTY QUANTIFICATION
International Journal for Uncertainty Quantification, Vol.1, 2011, issue 4
Oleg Roderick, Mihai Anitescu, Fred Hickernell, Yiou Li