Toward a more robust variance-based global sensitivity analysis of model outputs

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Global sensitivity analysis (GSA) measures the variation of a model output as a function of the variations of the model inputs given their ranges. In this paper we consider variance-based GSA methods that do not rely on certain assumptions about the model structure such as linearity or monotonicity. These variance-based methods decompose the output variance into terms of increasing dimensionality called 'sensitivity indices', first introduced by Sobol' [25]. Sobol' developed a method of estimating these sensitivity indices using Monte Carlo simulations. McKay [13] proposed an efficient method using replicated Latin hypercube sampling to compute the 'correlation ratios' or 'main effects', ... continued below

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Tong, C October 15, 2007.

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Global sensitivity analysis (GSA) measures the variation of a model output as a function of the variations of the model inputs given their ranges. In this paper we consider variance-based GSA methods that do not rely on certain assumptions about the model structure such as linearity or monotonicity. These variance-based methods decompose the output variance into terms of increasing dimensionality called 'sensitivity indices', first introduced by Sobol' [25]. Sobol' developed a method of estimating these sensitivity indices using Monte Carlo simulations. McKay [13] proposed an efficient method using replicated Latin hypercube sampling to compute the 'correlation ratios' or 'main effects', which have been shown to be equivalent to Sobol's first-order sensitivity indices. Practical issues with using these variance estimators are how to choose adequate sample sizes and how to assess the accuracy of the results. This paper proposes a modified McKay main effect method featuring an adaptive procedure for accuracy assessment and improvement. We also extend our adaptive technique to the computation of second-order sensitivity indices. Details of the proposed adaptive procedure as wells as numerical results are included in this paper.

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PDF-file: 20 pages; size: 0.8 Mbytes

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  • Report No.: UCRL-TR-235561
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/923115 | External Link
  • Office of Scientific & Technical Information Report Number: 923115
  • Archival Resource Key: ark:/67531/metadc899225

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  • October 15, 2007

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  • Sept. 27, 2016, 1:39 a.m.

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  • Dec. 8, 2016, 11:59 p.m.

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Tong, C. Toward a more robust variance-based global sensitivity analysis of model outputs, report, October 15, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc899225/: accessed August 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.