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Regional sensitivity analysis using revised mean and variance ratio functions

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  • Wei, Pengfei
  • Lu, Zhenzhou
  • Ruan, Wenbin
  • Song, Jingwen

Abstract

The variance ratio function, derived from the contribution to sample variance (CSV) plot, is a regional sensitivity index for studying how much the output deviates from the original mean of model output when the distribution range of one input is reduced and to measure the contribution of different distribution ranges of each input to the variance of model output. In this paper, the revised mean and variance ratio functions are developed for quantifying the actual change of the model output mean and variance, respectively, when one reduces the range of one input. The connection between the revised variance ratio function and the original one is derived and discussed. It is shown that compared with the classical variance ratio function, the revised one is more suitable to the evaluation of model output variance due to reduced ranges of model inputs. A Monte Carlo procedure, which needs only a set of samples for implementing it, is developed for efficiently computing the revised mean and variance ratio functions. The revised mean and variance ratio functions are compared with the classical ones by using the Ishigami function. At last, they are applied to a planar 10-bar structure.

Suggested Citation

  • Wei, Pengfei & Lu, Zhenzhou & Ruan, Wenbin & Song, Jingwen, 2014. "Regional sensitivity analysis using revised mean and variance ratio functions," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 121-135.
    • Handle: RePEc:eee:reensy:v:121:y:2014:i:c:p:121-135
    DOI: 10.1016/j.ress.2013.08.001
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    References listed on IDEAS

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    Cited by:

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    3. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Regional and parametric sensitivity analysis of Sobol׳ indices," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 87-100.
    4. Pannier, S. & Graf, W., 2015. "Sectional global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 110-117.
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    6. Datteo, Alessio & Busca, Giorgio & Quattromani, Gianluca & Cigada, Alfredo, 2018. "On the use of AR models for SHM: A global sensitivity and uncertainty analysis framework," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 99-115.

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