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http://dx.doi.org/10.9723/jksiis.2010.15.5.179

Evaluation of Uncertainty Importance Measure for Monotonic Function  

Cho, Jae-Gyeun (동의대학교 e비즈니스학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.15, no.5, 2010 , pp. 179-185 More about this Journal
Abstract
In a sensitivity analysis, an uncertainty importance measure is often used to assess how much uncertainty of an output is attributable to the uncertainty of an input, and thus, to identify those inputs whose uncertainties need to be reduced to effectively reduce the uncertainty of output. A function is called monotonic if the output is either increasing or decreasing with respect to any of the inputs. In this paper, for a monotonic function, we propose a method for evaluating the measure which assesses the expected percentage reduction in the variance of output due to ascertaining the value of input. The proposed method can be applied to the case that the output is expressed as linear and nonlinear monotonic functions of inputs, and that the input follows symmetric and asymmetric distributions. In addition, the proposed method provides a stable uncertainty importance of each input by discretizing the distribution of input to the discrete distribution. However, the proposed method is computationally demanding since it is based on Monte Carlo simulation.
Keywords
Uncertainty Importance Measure; Monotonic Function;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 조재균, 정석찬, "결점나무 분석에서 불확실성 중요도 측도의 평가," 정보시스템연구, 제17권, 3호, pp.25-37, 2008.   과학기술학회마을
2 Saltelli, A., Tarantola, S., and Chan, K. P. S., "A quantitative model-independent method for global sensitivity analysis of model output," Technometrics, vol 41, no 1, pp.39-56, 1999.   DOI   ScienceOn
3 Borgonovo, E., "A new uncertainty importance measure," Reliability Engineering and System Safety, vol 92, pp.771-784, 2007.   DOI   ScienceOn
4 Rushdi, A. M., "Uncertainty analysis of fault-tree outputs," IEEE Trans. Reliability, vol R-34, pp.458-462, 1985.   DOI
5 Cho, J. G. and Yum, B. J., "Development and evaluation of an uncertainty importance measure in fault tree analysis," Reliability Engineering and System Safety, vol 57, pp.143-157, 1997.   DOI   ScienceOn
6 D'Errico, J. R. and Zaino Jr., N. A., "Statistical tolerancing using a modification of Taguchi' s method," Technometrics, vol 30, no 4, pp.397-405, 1988.   DOI   ScienceOn
7 Park, C. K. and Ahn, K. I., "A new approach for measuring uncertainty importance and distributional sensitivity in probabilistic safety assessment," Reliability Engineering and System Safety, vol 46, pp.253-261, 1994.   DOI   ScienceOn
8 Sobol, I. M., "Sensitivity estimates for nonlinear mathematical models," Mathematical Modeling and Computational Experiment, vol 1, no 4, pp.407-414, 1993.
9 Sobol, I. M., "Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates," Mathematics and Computers in Simulation, vol 55, no 1, pp.271-280, 2001.   DOI   ScienceOn
10 Saltelli, A., "Sensitivity analysis for importance assessment," Risk Analysis, vol 22, no 3, pp.579-590, 2002.   DOI   ScienceOn
11 Bier, V. M., "A measure of uncertainty importance for components in fault trees," Transactions of the 1983 Winter Meeting of the American Nuclear Society, vol 45, no 1, pp.384-385, 1983.
12 Helton, J. C., "Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal," Reliability Engineering and System Safety, vol 42, pp.327-367, 1993.   DOI
13 Frey, C. H. and Patil, S. R., "Identification and review of sensitivity analysis methods," Risk Analysis, vol 22, no 3, pp.553-571, 2002.   DOI   ScienceOn
14 Nakashima, K. and Yamato, K., "Variance-importance of system components," IEEE Trans. Reliability, vol R-31, pp.99-100, 1982.   DOI
15 Pan, Z. J. and Tai, Y. C., "Variance importance of system components by Monte Carlo," IEEE Trans. Reliability, vol 37, pp.421-423, 1988.   DOI   ScienceOn
16 Iman, R. L., "A matrix-based approach to uncertainty and sensitivity analysis for fault trees," Risk Analysis, vol 7, pp.21-33, 1987.   DOI   ScienceOn
17 Iman, R. L. and Hora, S. C., "A robust measure of uncertainty importance for use in fault tree system analysis," Risk Analysis, vol 10, pp.401-406, 1990.   DOI
18 Seo, H. S. and Kwak, B. M., "Efficient statistical tolerance analysis for general distributions using three-point information," International Journal of Production Research, vol 40, pp.931-944, 2002.   DOI   ScienceOn
19 Borgonovo, E., "Measuring uncertainty importance: investigation and comparison of alternative approaches," Risk Analysis, vol 26, no 5, pp.1349-1361, 2006.   DOI   ScienceOn
20 Saltelli, A. and Marivoet, J., "Non-parametric statistics in sensitivity analysis for model output: A comparison of selected techniques," Reliability Engineering and System Safety, vol 28, pp.229-253, 1990.   DOI   ScienceOn
21 Chun, M. H., Han, S. J. and Tak, N. I., "An uncertainty importance measure using a distance metric for the change in a cumulative distribution function," Reliability Engineering and System Safety, vol 70, pp.313-321, 2000.   DOI   ScienceOn