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The ordering importance measure of random variable and its estimation

Author

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  • Cui, Lijie
  • Lu, Zhenzhou
  • Wang, Pan
  • Wang, Weihu

Abstract

Based on the importance analysis with independent random variables, an ordering importance measure is proposed to evaluate the effects of variables on the uncertainty of output response first, in which not only independent random variables but also correlated ones are included, and it provides a theoretical basis to improve a system or a model. Secondly, the sampling strategy of the conditional probability density function is provided by the Copula transformation, which could solve the vital problem of the importance analysis effectively with correlated random variables. What's more, Due to the low efficiency and tremendous computational cost of the Monte Carlo method, the probability density function evolution method (PDEM) is utilized to solve the ordering importance measure. Finally, some examples in cases of independent random variables and correlated random variables are employed to demonstrate the feasibility and reasonability of the proposed measure, test the precision of the probability density function evolution method, even.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:105:y:2014:i:c:p:132-143
    DOI: 10.1016/j.matcom.2014.06.003
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    References listed on IDEAS

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    1. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    2. Saltelli A. & Tarantola S., 2002. "On the Relative Importance of Input Factors in Mathematical Models: Safety Assessment for Nuclear Waste Disposal," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 702-709, September.
    3. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    4. Feldman, Allan, 1979. "Manipulation and the Pareto rule," Journal of Economic Theory, Elsevier, vol. 21(3), pages 473-482, December.
    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.
    6. Andrea Saltelli, 2002. "Sensitivity Analysis for Importance Assessment," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 579-590, June.
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