IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v94y2009i2p237-243.html
   My bibliography  Save this article

Non-parametric estimation of conditional moments for sensitivity analysis

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832008000707
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2008.02.023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Ma, Yuan-Zhuo & Jin, Xiang-Xiang & Zhao, Xiang & Li, Hong-Shuang & Zhao, Zhen-Zhou & Xu, Chang, 2024. "Reliability-oriented global sensitivity analysis using subset simulation and space partition," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    2. Tianyang Wang & James S. Dyer & Warren J. Hahn, 2017. "Sensitivity analysis of decision making under dependent uncertainties using copulas," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 117-139, November.
    3. 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.
    4. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2019. "Reverse sensitivity testing: What does it take to break the model?," European Journal of Operational Research, Elsevier, vol. 274(2), pages 654-670.
    5. Ge, Qiao & Menendez, Monica, 2017. "Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 28-39.
    6. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    7. Melito, Gian Marco & Müller, Thomas Stephan & Badeli, Vahid & Ellermann, Katrin & Brenn, Günter & Reinbacher-Köstinger, Alice, 2021. "Sensitivity analysis study on the effect of the fluid mechanics assumptions for the computation of electrical conductivity of flowing human blood," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    8. 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.
    9. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    10. Sinan Xiao & Zhenzhou Lu & Pan Wang, 2018. "Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2703-2721, December.
    11. 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.
    12. Daneshkhah, Alireza & Bedford, Tim, 2013. "Probabilistic sensitivity analysis of system availability using Gaussian processes," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 82-93.
    13. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
    14. 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.
    15. Konakli, Katerina & Sudret, Bruno, 2016. "Global sensitivity analysis using low-rank tensor approximations," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 64-83.
    16. 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.
    17. Wang, Zhenqiang & Jia, Gaofeng, 2020. "Augmented sample-based approach for efficient evaluation of risk sensitivity with respect to epistemic uncertainty in distribution parameters," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    18. Manel Baucells & Emanuele Borgonovo, 2013. "Invariant Probabilistic Sensitivity Analysis," Management Science, INFORMS, vol. 59(11), pages 2536-2549, November.
    19. Xie, Xiangzhong & Schenkendorf, René & Krewer, Ulrike, 2019. "Efficient sensitivity analysis and interpretation of parameter correlations in chemical engineering," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 159-173.
    20. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:94:y:2009:i:2:p:237-243. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.