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Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study

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  • Zhai, Qingqing
  • Yang, Jun
  • Zhao, Yu

Abstract

Variance-based sensitivity analysis has been widely studied and asserted itself among practitioners. Monte Carlo simulation methods are well developed in the calculation of variance-based sensitivity indices but they do not make full use of each model run. Recently, several works mentioned a scatter-plot partitioning method to estimate the variance-based sensitivity indices from given data, where a single bunch of samples is sufficient to estimate all the sensitivity indices. This paper focuses on the space-partition method in the estimation of variance-based sensitivity indices, and its convergence and other performances are investigated. Since the method heavily depends on the partition scheme, the influence of the partition scheme is discussed and the optimal partition scheme is proposed based on the minimized estimator׳s variance. A decomposition and integration procedure is proposed to improve the estimation quality for higher order sensitivity indices. The proposed space-partition method is compared with the more traditional method and test cases show that it outperforms the traditional one.

Suggested Citation

  • Zhai, Qingqing & Yang, Jun & Zhao, Yu, 2014. "Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 66-82.
    • Handle: RePEc:eee:reensy:v:131:y:2014:i:c:p:66-82
    DOI: 10.1016/j.ress.2014.06.013
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    1. Blatman, Géraud & Sudret, Bruno, 2010. "Efficient computation of global sensitivity indices using sparse polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1216-1229.
    2. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
    3. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    4. Ye, Zhisheng & Li, Zhizhong & Xie, Min, 2010. "Some improvements on adaptive genetic algorithms for reliability-related applications," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 120-126.
    5. Hong-Zhong Huang & Jian Qu & Ming Zuo, 2009. "Genetic-algorithm-based optimal apportionment of reliability and redundancy under multiple objectives," IISE Transactions, Taylor & Francis Journals, vol. 41(4), pages 287-298.
    6. Xu, Chonggang & Gertner, George Zdzislaw, 2008. "A general first-order global sensitivity analysis method," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1060-1071.
    7. Xu, C. & Gertner, G., 2007. "Extending a global sensitivity analysis technique to models with correlated parameters," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5579-5590, August.
    8. Zio, Enrico & Podofillini, Luca, 2006. "Accounting for components interactions in the differential importance measure," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1163-1174.
    9. Rahman, Sharif, 2011. "Global sensitivity analysis by polynomial dimensional decomposition," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 825-837.
    10. Plischke, Elmar, 2012. "An adaptive correlation ratio method using the cumulative sum of the reordered output," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 149-156.
    11. Tarantola, S. & Gatelli, D. & Mara, T.A., 2006. "Random balance designs for the estimation of first order global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 717-727.
    12. Xu, Chonggang & Gertner, George, 2011. "Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST)," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 184-198, January.
    13. Peng, Rui & Mo, Huadong & Xie, Min & Levitin, Gregory, 2013. "Optimal structure of multi-state systems with multi-fault coverage," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 18-25.
    14. Oladyshkin, S. & Nowak, W., 2012. "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 179-190.
    15. Haro Sandoval, Eduardo & Anstett-Collin, Floriane & Basset, Michel, 2012. "Sensitivity study of dynamic systems using polynomial chaos," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 15-26.
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