IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v95y2010i3p301-309.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832009002403
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2009.10.003?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. Tong, Charles, 2006. "Refinement strategies for stratified sampling methods," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1257-1265.
    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. 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.

    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. Jin Xu & Jiajie Chen & Peter Z. G. Qian, 2015. "Sequentially Refined Latin Hypercube Designs: Reusing Every Point," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1696-1706, December.
    2. Shields, Michael D. & Teferra, Kirubel & Hapij, Adam & Daddazio, Raymond P., 2015. "Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 310-325.
    3. 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.
    4. Sallaberry, C.J. & Helton, J.C. & Hora, S.C., 2008. "Extension of Latin hypercube samples with correlated variables," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1047-1059.
    5. Munoz Zuniga, M. & Garnier, J. & Remy, E. & de Rocquigny, E., 2011. "Adaptive directional stratification for controlled estimation of the probability of a rare event," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1691-1712.
    6. Qing Deng & Changsen Feng & Fushuan Wen & Chung-Li Tseng & Lei Wang & Bo Zou & Xizhu Zhang, 2019. "Evaluation of Accommodation Capability for Electric Vehicles of a Distribution System Considering Coordinated Charging Strategies," Energies, MDPI, vol. 12(16), pages 1-20, August.
    7. A. M. Elsawah & Kai-Tai Fang, 2020. "New foundations for designing U-optimal follow-up experiments with flexible levels," Statistical Papers, Springer, vol. 61(2), pages 823-849, April.
    8. Sui Peng & Huixiang Chen & Yong Lin & Tong Shu & Xingyu Lin & Junjie Tang & Wenyuan Li & Weijie Wu, 2019. "Probabilistic Power Flow for Hybrid AC/DC Grids with Ninth-Order Polynomial Normal Transformation and Inherited Latin Hypercube Sampling," Energies, MDPI, vol. 12(16), pages 1-21, August.
    9. Sakurahara, Tatsuya & O'Shea, Nicholas & Cheng, Wen-Chi & Zhang, Sai & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "Integrating renewal process modeling with Probabilistic Physics-of-Failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.

    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:95:y:2010:i:3:p:301-309. 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.