IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v212y2021ics0951832021000806.html
   My bibliography  Save this article

Multivariate sensitivity analysis and derivative-based global sensitivity measures with dependent variables

Author

Listed:
  • Lamboni, Matieyendou
  • Kucherenko, Sergei

Abstract

In this paper, we propose a new methodology for better assessing the single, overall and interactions contributions of dependent and/or correlated variables over the whole model outputs. Our methodology relies on our ability to extract a model that characterizes the dependency structures of any random vector. Such dependency model is then coupled with the initial model to perform uncertainty quantification, variance-based sensitivity analysis and derivative-based global sensitivity measures. Our methodology allows for defining the main-effect and total sensitivity indices of input(s) with the former index less than the latter. We provide derivative-based upper bounds of total indices, which can be used for screening dependent variables. We also extend Morris’ methods to cope with dependent variables. For proposing such indices, we distinguish the case of the multivariate and/or functional outputs.

Suggested Citation

  • Lamboni, Matieyendou & Kucherenko, Sergei, 2021. "Multivariate sensitivity analysis and derivative-based global sensitivity measures with dependent variables," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
  • Handle: RePEc:eee:reensy:v:212:y:2021:i:c:s0951832021000806
    DOI: 10.1016/j.ress.2021.107519
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021000806
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2021.107519?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. Lamboni, Matieyendou, 2021. "Derivative-based integral equalities and inequality: A proxy-measure for sensitivity analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 137-161.
    2. Elja Arjas & Tapani Lehtonen, 1978. "Approximating Many Server Queues by Means of Single Server Queues," Mathematics of Operations Research, INFORMS, vol. 3(3), pages 205-223, August.
    3. Hao, Wenrui & Lu, Zhenzhou & Wei, Pengfei, 2013. "Uncertainty importance measure for models with correlated normal variables," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 48-58.
    4. Roustant, O. & Fruth, J. & Iooss, B. & Kuhnt, S., 2014. "Crossed-derivative based sensitivity measures for interaction screening," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 105(C), pages 105-118.
    5. Lamboni, Matieyendou, 2020. "Derivative-based generalized sensitivity indices and Sobol’ indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 236-256.
    6. Lamboni, M. & Iooss, B. & Popelin, A.-L. & Gamboa, F., 2013. "Derivative-based global sensitivity measures: General links with Sobol’ indices and numerical tests," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 87(C), pages 45-54.
    7. Xiao, Sinan & Lu, Zhenzhou & Xu, Liyang, 2017. "Multivariate sensitivity analysis based on the direction of eigen space through principal component analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 1-10.
    8. Mara, Thierry A. & Tarantola, Stefano, 2012. "Variance-based sensitivity indices for models with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 115-121.
    9. Lamboni, Matieyendou & Monod, Hervé & Makowski, David, 2011. "Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 450-459.
    10. Lamboni, Matieyendou, 2019. "Multivariate sensitivity analysis: Minimum variance unbiased estimators of the first-order and total-effect covariance matrices," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 67-92.
    11. Sobol’, I.M. & Kucherenko, S., 2009. "Derivative based global sensitivity measures and their link with global sensitivity indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3009-3017.
    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. Matieyendou Lamboni, 2023. "On Exact Distribution for Multivariate Weighted Distributions and Classification," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-26, March.
    2. Vuillod, Bruno & Montemurro, Marco & Panettieri, Enrico & Hallo, Ludovic, 2023. "A comparison between Sobol’s indices and Shapley’s effect for global sensitivity analysis of systems with independent input variables," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Luo, Chunling & Shen, Lijuan & Xu, Ancha, 2022. "Modelling and estimation of system reliability under dynamic operating environments and lifetime ordering constraints," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    4. Lamboni, Matieyendou, 2022. "Efficient dependency models: Simulating dependent random variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 199-217.
    5. Lamboni, Matieyendou, 2022. "Weak derivative-based expansion of functions: ANOVA and some inequalities," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 194(C), pages 691-718.
    6. Shi, Wen & Zhou, Qing & Zhou, Yanju, 2023. "An efficient elementary effect-based method for sensitivity analysis in identifying main and two-factor interaction effects," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

    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. Lamboni, Matieyendou, 2021. "Derivative-based integral equalities and inequality: A proxy-measure for sensitivity analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 137-161.
    2. Lamboni, Matieyendou, 2022. "Weak derivative-based expansion of functions: ANOVA and some inequalities," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 194(C), pages 691-718.
    3. Lamboni, Matieyendou, 2020. "Derivative-based generalized sensitivity indices and Sobol’ indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 236-256.
    4. 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.
    5. Matieyendou Lamboni, 2024. "Kernel-based Measures of Association Between Inputs and Outputs Using ANOVA," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(2), pages 790-826, August.
    6. Matieyendou Lamboni, 2020. "Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices," Statistical Papers, Springer, vol. 61(5), pages 1939-1970, October.
    7. Lamboni, Matieyendou, 2022. "Efficient dependency models: Simulating dependent random variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 199-217.
    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. 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.
    10. Lamboni, Matieyendou, 2019. "Multivariate sensitivity analysis: Minimum variance unbiased estimators of the first-order and total-effect covariance matrices," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 67-92.
    11. Chen, Xin & Molina-Cristóbal, Arturo & Guenov, Marin D. & Riaz, Atif, 2019. "Efficient method for variance-based sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 97-115.
    12. Matieyendou Lamboni, 2018. "Global sensitivity analysis: a generalized, unbiased and optimal estimator of total-effect variance," Statistical Papers, Springer, vol. 59(1), pages 361-386, March.
    13. Fruth, J. & Roustant, O. & Kuhnt, S., 2019. "Support indices: Measuring the effect of input variables over their supports," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 17-27.
    14. Matieyendou Lamboni, 2024. "Optimal Estimators of Cross-Partial Derivatives and Surrogates of Functions," Stats, MDPI, vol. 7(3), pages 1-22, July.
    15. Isadora Antoniano‐Villalobos & Emanuele Borgonovo & Sumeda Siriwardena, 2018. "Which Parameters Are Important? Differential Importance Under Uncertainty," Risk Analysis, John Wiley & Sons, vol. 38(11), pages 2459-2477, November.
    16. Song, Shufang & Zhou, Tong & Wang, Lu & Kucherenko, Sergei & Lu, Zhenzhou, 2019. "Derivative-based new upper bound of Sobol’ sensitivity measure," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 142-148.
    17. Xiao, Sinan & Lu, Zhenzhou & Wang, Pan, 2018. "Multivariate global sensitivity analysis for dynamic models based on wavelet analysis," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 20-30.
    18. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    19. Roustant, O. & Fruth, J. & Iooss, B. & Kuhnt, S., 2014. "Crossed-derivative based sensitivity measures for interaction screening," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 105(C), pages 105-118.
    20. 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.

    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:212:y:2021:i:c:s0951832021000806. 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.