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A new algorithm for variance-based importance measures and importance kernel sensitivity

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  • Changcong Zhou
  • Zhenzhou Lu
  • Guijie Li

Abstract

Variance-based importance measure has proven itself as an effective tool to reflect the effects of input variables on the output. Owing to the desirable properties, researchers have paid lots of attention to improving efficiency in computing a variance-based importance measure. Based on the theory of point estimate, this article proposes a new algorithm, decomposing the importance measure into inner and outer parts, and computing each part with a point estimate method. In order to discuss the impacts on the variance-based importance measure from distribution parameters of input variables, a new concept of kernel sensitivity of the variance-based importance measure is put forward, with solving algorithms respectively, based on numerical simulation and point estimate established as well. For cases where the performance function with independent and normally distributed input variables is expressed by a linear or quadratic polynomial without cross-terms, analytical results of the variance-based importance measure and the kernel sensitivity are derived. Numerical and engineering examples have been employed to illustrate the applicability of the proposed concept and algorithm.

Suggested Citation

  • Changcong Zhou & Zhenzhou Lu & Guijie Li, 2013. "A new algorithm for variance-based importance measures and importance kernel sensitivity," Journal of Risk and Reliability, , vol. 227(1), pages 16-27, February.
    • Handle: RePEc:sae:risrel:v:227:y:2013:i:1:p:16-27
    DOI: 10.1177/1748006X12467590
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    References listed on IDEAS

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    1. Yu, W. & Harris, T.J., 2009. "Parameter uncertainty effects on variance-based sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 596-603.
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    3. Tong, Charles, 2010. "Self-validated variance-based methods for sensitivity analysis of model outputs," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 301-309.
    4. Andrea Saltelli, 2002. "Sensitivity Analysis for Importance Assessment," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 579-590, June.
    5. Castillo, Enrique & Mínguez, Roberto & Castillo, Carmen, 2008. "Sensitivity analysis in optimization and reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 93(12), pages 1788-1800.
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