Global sensitivity measures from given data

被引:288
作者
Plischke, Elmar [1 ]
Borgonovo, Emanuele [2 ]
Smith, Curtis L. [3 ]
机构
[1] Tech Univ Clausthal, Inst Disposal Res, D-38678 Clausthal Zellerfeld, Germany
[2] Bocconi Univ, Dept Decis Sci, I-20136 Milan, Italy
[3] Idaho Natl Lab, Idaho Falls, ID 83415 USA
关键词
Uncertainty analysis; Global sensitivity analysis; Simulation; UNCERTAINTY IMPORTANCE; SIMULATION EXPERIMENTS; ENGINEERING SYSTEMS; SAMPLING PLANS; SPECIAL-ISSUE; MODELS; DESIGNS; BOOTSTRAP; VARIANCE; INDEXES;
D O I
10.1016/j.ejor.2012.11.047
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Simulation models support managers in the solution of complex problems. International agencies recommend uncertainty and global sensitivity methods as best practice in the audit, validation and application of scientific codes. However, numerical complexity, especially in the presence of a high number of factors, induces analysts to employ less informative but numerically cheaper methods. This work introduces a design for estimating global sensitivity indices from given data (including simulation input-output data), at the minimum computational cost. We address the problem starting with a statistic based on the L-1-norm. A formal definition of the estimators is provided and corresponding consistency theorems are proved. The determination of confidence intervals through a bias-reducing bootstrap estimator is investigated. The strategy is applied in the identification of the key drivers of uncertainty for the complex computer code developed at the National Aeronautics and Space Administration (NASA) assessing the risk of lunar space missions. We also introduce a symmetry result that enables the estimation of global sensitivity measures to datasets produced outside a conventional input-output functional framework. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:536 / 550
页数:15
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