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A new computational method of a moment-independent uncertainty importance measure

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  • Liu, Qiao
  • Homma, Toshimitsu

Abstract

For a risk assessment model, the uncertainty in input parameters is propagated through the model and leads to the uncertainty in the model output. The study of how the uncertainty in the output of a model can be apportioned to the uncertainty in the model inputs is the job of sensitivity analysis. Saltelli [Sensitivity analysis for importance assessment. Risk Analysis 2002;22(3):579–90] pointed out that a good sensitivity indicator should be global, quantitative and model free. Borgonovo [A new uncertainty importance measure. Reliability Engineering and System Safety 2007;92(6):771–84] further extended these three requirements by adding the fourth feature, moment-independence, and proposed a new sensitivity measure, δi. It evaluates the influence of the input uncertainty on the entire output distribution without reference to any specific moment of the model output. In this paper, a new computational method of δi is proposed. It is conceptually simple and easier to implement. The feasibility of this new method is proved by applying it to two examples.

Suggested Citation

  • Liu, Qiao & Homma, Toshimitsu, 2009. "A new computational method of a moment-independent uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1205-1211.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:7:p:1205-1211
    DOI: 10.1016/j.ress.2008.10.005
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    References listed on IDEAS

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    1. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    2. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    3. Sumeet R. Patil & H. Christopher Frey, 2004. "Comparison of Sensitivity Analysis Methods Based on Applications to a Food Safety Risk Assessment Model," Risk Analysis, John Wiley & Sons, vol. 24(3), pages 573-585, June.
    4. Borgonovo, E. & Peccati, L., 2006. "Uncertainty and global sensitivity analysis in the evaluation of investment projects," International Journal of Production Economics, Elsevier, vol. 104(1), pages 62-73, November.
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