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Parameter uncertainty effects on variance-based sensitivity analysis

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  • Yu, W.
  • Harris, T.J.

Abstract

In the past several years there has been considerable commercial and academic interest in methods for variance-based sensitivity analysis. The industrial focus is motivated by the importance of attributing variance contributions to input factors. A more complete understanding of these relationships enables companies to achieve goals related to quality, safety and asset utilization. In a number of applications, it is possible to distinguish between two types of input variables—regressive variables and model parameters. Regressive variables are those that can be influenced by process design or by a control strategy. With model parameters, there are typically no opportunities to directly influence their variability. In this paper, we propose a new method to perform sensitivity analysis through a partitioning of the input variables into these two groupings: regressive variables and model parameters. A sequential analysis is proposed, where first an sensitivity analysis is performed with respect to the regressive variables. In the second step, the uncertainty effects arising from the model parameters are included. This strategy can be quite useful in understanding process variability and in developing strategies to reduce overall variability. When this method is used for nonlinear models which are linear in the parameters, analytical solutions can be utilized. In the more general case of models that are nonlinear in both the regressive variables and the parameters, either first order approximations can be used, or numerically intensive methods must be used.

Suggested Citation

  • Yu, W. & Harris, T.J., 2009. "Parameter uncertainty effects on variance-based sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 596-603.
  • Handle: RePEc:eee:reensy:v:94:y:2009:i:2:p:596-603
    DOI: 10.1016/j.ress.2008.06.016
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    References listed on IDEAS

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    1. Saltelli, Andrea & Ratto, Marco & Tarantola, Stefano & Campolongo, Francesca, 2006. "Sensitivity analysis practices: Strategies for model-based inference," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1109-1125.
    2. 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.
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    Cited by:

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    2. Wenping Xu & Zongjun Wang & Liu Hong & Ligang He & Xueguang Chen, 2015. "The uncertainty recovery analysis for interdependent infrastructure systems using the dynamic inoperability input–output model," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(7), pages 1299-1306, May.
    3. Liu, Min & He, Honglin & Ren, Xiaoli & Sun, Xiaomin & Yu, Guirui & Han, Shijie & Wang, Huimin & Zhou, Guoyi, 2015. "The effects of constraining variables on parameter optimization in carbon and water flux modeling over different forest ecosystems," Ecological Modelling, Elsevier, vol. 303(C), pages 30-41.
    4. Guijie Li & Zhenzhou Lu & Longfei Tian & Jia Xu, 2013. "The importance measure on the non-probabilistic reliability index of uncertain structures," Journal of Risk and Reliability, , vol. 227(6), pages 651-661, December.
    5. Wang, Wei & Maio, Francesco Di & Zio, Enrico, 2017. "Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 276-289.
    6. Ramkishore Singh & Dharam Buddhi & Samar Thapa & Chander Prakash & Rajesh Singh & Atul Sharma & Shane Sheoran & Kuldeep Kumar Saxena, 2022. "Sensitivity Analysis for Decisive Design Parameters for Energy and Indoor Visual Performances of a Glazed Façade Office Building," Sustainability, MDPI, vol. 14(21), pages 1-27, October.

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