嵌套变量集上Sobol指数的测试比较

IF 2.1 3区工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2022-03-31 DOI:10.1137/21m1457370

T. Klein, Nicolas Peteilh, P. Rochet

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引用次数: 1

摘要

灵敏度指数通常用于量化任何特定输入变量组对计算机代码输出的相对影响。一个关键的问题是确定给定的一组变量是否对输出有重大影响。Sobol指数通常用于测量这种影响，但它们的估计可能很困难，因为它们通常需要特定的实验设计。在这项工作中，我们利用Sobol指标相对于集合包含的单调性来测试一些输入变量的影响。该方法不依赖于Sobol指数的直接估计，可以在经典的iid抽样设计下进行。

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Test Comparison for Sobol Indices over Nested Sets of Variables

Sensitivity indices are commonly used to quantify the relative inﬂuence of any speciﬁc group of input variables on the output of a computer code. One crucial question is then to decide whether a given set of variables has a signiﬁcant impact on the output. Sobol indices are often used to measure this impact but their estimation can be diﬃcult as they usually require a particular design of experiment. In this work, we take advantage of the monotonicity of Sobol indices with respect to set inclusion to test the inﬂuence of some of the input variables. The method does not rely on a direct estimation of the Sobol indices and can be performed under classical iid sampling designs.

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来源期刊

Siam-Asa Journal on Uncertainty Quantification Mathematics-Statistics and Probability

CiteScore

3.70

自引率

0.00%

发文量

期刊介绍： SIAM/ASA Journal on Uncertainty Quantification (JUQ) publishes research articles presenting significant mathematical, statistical, algorithmic, and application advances in uncertainty quantification, defined as the interface of complex modeling of processes and data, especially characterizations of the uncertainties inherent in the use of such models. The journal also focuses on related fields such as sensitivity analysis, model validation, model calibration, data assimilation, and code verification. The journal also solicits papers describing new ideas that could lead to significant progress in methodology for uncertainty quantification as well as review articles on particular aspects. The journal is dedicated to nurturing synergistic interactions between the mathematical, statistical, computational, and applications communities involved in uncertainty quantification and related areas. JUQ is jointly offered by SIAM and the American Statistical Association.