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Non-parametric estimation of conditional moments for sensitivity analysis

Author

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  • Ratto, M.
  • Pagano, A.
  • Young, P.C.

Abstract

In this paper, we consider the non-parametric estimation of conditional moments, which is useful for applications in global sensitivity analysis (GSA) and in the more general emulation framework. The estimation is based on the state-dependent parameter (SDP) estimation approach and allows for the estimation of conditional moments of order larger than unity. This allows one to identify a wider spectrum of parameter sensitivities with respect to the variance-based main effects, like shifts in the variance, skewness or kurtosis of the model output, so adding valuable information for the analyst, at a small computational cost.

Suggested Citation

  • Ratto, M. & Pagano, A. & Young, P.C., 2009. "Non-parametric estimation of conditional moments for sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 237-243.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:2:p:237-243
    DOI: 10.1016/j.ress.2008.02.023
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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. 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.
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    Cited by:

    1. Wang, Pan & Lu, Zhenzhou & Zhang, Kaichao & Xiao, Sinan & Yue, Zhufeng, 2018. "Copula-based decomposition approach for the derivative-based sensitivity of variance contributions with dependent variables," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 437-450.
    2. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    3. Cheng, Lei & Lu, Zhenzhou & Zhang, Leigang, 2015. "Application of Rejection Sampling based methodology to variance based parametric sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 9-18.
    4. 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.
    5. Xiao, Sinan & Lu, Zhenzhou & Xu, Liyang, 2016. "A new effective screening design for structural sensitivity analysis of failure probability with the epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 1-14.
    6. 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.
    7. Li, Luyi & Lu, Zhenzhou & Hu, JiXiang, 2014. "A new kind of regional importance measure of the input variable and its state dependent parameter solution," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 1-16.
    8. Wenbin Ruan & Zhenzhou Lu & Longfei Tian, 2013. "A modified variance-based importance measure and its solution by state dependent parameter," Journal of Risk and Reliability, , vol. 227(1), pages 3-15, February.
    9. Wei, Pengfei & Lu, Zhenzhou & Yuan, Xiukai, 2013. "Monte Carlo simulation for moment-independent sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 60-67.
    10. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
    11. Allaire, Douglas L. & Willcox, Karen E., 2012. "A variance-based sensitivity index function for factor prioritization," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 107-114.
    12. Wenbin Ruan & Zhenzhou Lu & Pengfei Wei, 2013. "Estimation of conditional moment by moving least squares and its application for importance analysis," Journal of Risk and Reliability, , vol. 227(6), pages 641-650, December.
    13. Luo, Xiaopeng & Lu, Zhenzhou & Xu, Xin, 2014. "Non-parametric kernel estimation for the ANOVA decomposition and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 140-148.
    14. Attar, Peter J. & Vedula, Prakash, 2013. "On convergence of moments in uncertainty quantification based on direct quadrature," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 119-125.

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