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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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.
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