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Self-validated variance-based methods for sensitivity analysis of model outputs

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  • Tong, Charles

Abstract

Global sensitivity analysis (GSA) has the advantage over local sensitivity analysis in that GSA does not require strong model assumptions such as linearity or monotonicity. As a result, GSA methods such as those based on variance decomposition are well-suited to multi-physics models, which are often plagued by large nonlinearities. However, as with many other sampling-based methods, inadequate sample size can badly pollute the result accuracies. A natural remedy is to adaptively increase the sample size until sufficient accuracy is obtained. This paper proposes an iterative methodology comprising mechanisms for guiding sample size selection and self-assessing result accuracy. The elegant features in the proposed methodology are the adaptive refinement strategies for stratified designs. We first apply this iterative methodology to the design of a self-validated first-order sensitivity analysis algorithm. We also extend this methodology to propose a self-validated second-order sensitivity analysis algorithm based on refining replicated orthogonal array designs. Several numerical experiments are given to demonstrate the effectiveness of these methods.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:3:p:301-309
    DOI: 10.1016/j.ress.2009.10.003
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    1. Tong, Charles, 2006. "Refinement strategies for stratified sampling methods," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1257-1265.
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    Cited by:

    1. 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.
    2. Jin Tian & Yue Li, 2014. "Factors influencing cost-effectiveness of maintenance of power distribution poles subjected to hurricanes: a system-dynamics-based analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 633-650, June.
    3. Zitrou, A. & Bedford, T. & Daneshkhah, A., 2013. "Robustness of maintenance decisions: Uncertainty modelling and value of information," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 60-71.
    4. DeJonge, Kendall C. & Ascough, James C. & Ahmadi, Mehdi & Andales, Allan A. & Arabi, Mazdak, 2012. "Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments," Ecological Modelling, Elsevier, vol. 231(C), pages 113-125.

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