IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v107y2012icp3-15.html
   My bibliography  Save this article

Development of a localized probabilistic sensitivity method to determine random variable regional importance

Author

Listed:
  • Millwater, Harry
  • Singh, Gulshan
  • Cortina, Miguel

Abstract

There are many methods to identify the important variable out of a set of random variables, i.e., “inter-variable†importance; however, to date there are no comparable methods to identify the “region†of importance within a random variable, i.e., “intra-variable†importance. Knowledge of the critical region of an input random variable (tail, near-tail, and central region) can provide valuable information towards characterizing, understanding, and improving a model through additional modeling or testing. As a result, an intra-variable probabilistic sensitivity method was developed and demonstrated for independent random variables that computes the partial derivative of a probabilistic response with respect to a localized perturbation in the CDF values of each random variable. These sensitivities are then normalized in absolute value with respect to the largest sensitivity within a distribution to indicate the region of importance. The methodology is implemented using the Score Function kernel-based method such that existing samples can be used to compute sensitivities for negligible cost. Numerical examples demonstrate the accuracy of the method through comparisons with finite difference and numerical integration quadrature estimates.

Suggested Citation

  • Millwater, Harry & Singh, Gulshan & Cortina, Miguel, 2012. "Development of a localized probabilistic sensitivity method to determine random variable regional importance," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 3-15.
    • Handle: RePEc:eee:reensy:v:107:y:2012:i:c:p:3-15
    DOI: 10.1016/j.ress.2011.04.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832011000974
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2011.04.003?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. Wu, Y-T. & Mohanty, Sitakanta, 2006. "Variable screening and ranking using sampling-based sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 634-647.
    2. Kleijnen, Jack P. C. & Rubinstein, Reuven Y., 1996. "Optimization and sensitivity analysis of computer simulation models by the score function method," European Journal of Operational Research, Elsevier, vol. 88(3), pages 413-427, February.
    3. H. Millwater & A. Bates & E. Vazquez, 2011. "Probabilistic sensitivity methods for correlated normal variables," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 5(1), pages 1-20.
    4. Kleijnen, J.P.C. & Rubinstein, R.Y., 1996. "Optimization and Sensitivity Analysis of Computer Simulation Models by the Score Function Method," Other publications TiSEM 958c9b9a-544f-48f3-a3d1-c, Tilburg University, School of Economics and Management.
    5. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    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. Wang, Pan & Lu, Zhenzhou & Ren, Bo & Cheng, Lei, 2013. "The derivative based variance sensitivity analysis for the distribution parameters and its computation," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 305-315.
    2. Chabridon, Vincent & Balesdent, Mathieu & Bourinet, Jean-Marc & Morio, Jérôme & Gayton, Nicolas, 2018. "Reliability-based sensitivity estimators of rare event probability in the presence of distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 164-178.

    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. Wang, Pan & Lu, Zhenzhou & Ren, Bo & Cheng, Lei, 2013. "The derivative based variance sensitivity analysis for the distribution parameters and its computation," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 305-315.
    2. Mingbin Ben Feng & Eunhye Song, 2020. "Efficient Nested Simulation Experiment Design via the Likelihood Ratio Method," Papers 2008.13087, arXiv.org, revised May 2024.
    3. Tan, S.Y.G.L. & van Oortmarssen, G.J. & Piersma, N., 2000. "Estimting parameters of a microsimulation model for breast cancer screening using the score function method," Econometric Institute Research Papers EI 2000-35/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Arsham Hossein, 2007. "Monte Carlo Techniques for Parametric Finite Multidimensional Integral Equations," Monte Carlo Methods and Applications, De Gruyter, vol. 13(3), pages 173-195, August.
    5. Dang, Ou & Feng, Mingbin & Hardy, Mary R., 2023. "Two-stage nested simulation of tail risk measurement: A likelihood ratio approach," Insurance: Mathematics and Economics, Elsevier, vol. 108(C), pages 1-24.
    6. 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.
    7. Shih, Neng-Hui, 1999. "The sensitivity analysis of binary networks via simulation," European Journal of Operational Research, Elsevier, vol. 114(3), pages 602-609, May.
    8. Zhou, Yuekuan & Zheng, Siqian, 2020. "Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method," Energy, Elsevier, vol. 193(C).
    9. Helton, Jon C. & Johnson, Jay D. & Sallaberry, Cédric J., 2011. "Quantification of margins and uncertainties: Example analyses from reactor safety and radioactive waste disposal involving the separation of aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1014-1033.
    10. Saurbayeva, Assemgul & Memon, Shazim Ali & Kim, Jong, 2023. "Integrated multi-stage sensitivity analysis and multi-objective optimization approach for PCM integrated residential buildings in different climate zones," Energy, Elsevier, vol. 278(PB).
    11. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    12. Guo, Zehua & Dailey, Ryan & Feng, Tangtao & Zhou, Yukun & Sun, Zhongning & Corradini, Michael L & Wang, Jun, 2021. "Uncertainty analysis of ATF Cr-coated-Zircaloy on BWR in-vessel accident progression during a station blackout," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    13. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    14. Zio, E. & Pedroni, N., 2010. "An optimized Line Sampling method for the estimation of the failure probability of nuclear passive systems," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1300-1313.
    15. Yildiz, Yusuf & Korkmaz, Koray & Göksal Özbalta, Türkan & Durmus Arsan, Zeynep, 2012. "An approach for developing sensitive design parameter guidelines to reduce the energy requirements of low-rise apartment buildings," Applied Energy, Elsevier, vol. 93(C), pages 337-347.
    16. Wu, Qiong-Li & Cournède, Paul-Henry & Mathieu, Amélie, 2012. "An efficient computational method for global sensitivity analysis and its application to tree growth modelling," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 35-43.
    17. Zio, E. & Pedroni, N., 2012. "Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 90-106.
    18. Song, Xiaodong & Bryan, Brett A. & Almeida, Auro C. & Paul, Keryn I. & Zhao, Gang & Ren, Yin, 2013. "Time-dependent sensitivity of a process-based ecological model," Ecological Modelling, Elsevier, vol. 265(C), pages 114-123.
    19. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    20. Cui, Lijie & Lu, Zhenzhou & Wang, Pan & Wang, Weihu, 2014. "The ordering importance measure of random variable and its estimation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 105(C), pages 132-143.

    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:107:y:2012:i:c:p:3-15. 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.