A new uncertainty importance measure

Reliability Engineering and System Safety

Volumn 92, Issue 6, 2007, Pages 771-784

  Borgonovo, E ^a  

^a BOCCONI UNIVERSITY   (Italy)

Author keywords

Global sensitivity analysis; Importance measures; Probabilistic risk assessment; Uncertainty analysis; Uncertainty importance measures

Indexed keywords

CORRELATION METHODS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBABILISTIC LOGICS; RISK ASSESSMENT; SENSITIVITY ANALYSIS;

GLOBAL SENSITIVITY ANALYSIS; IMPORTANCE MEASURES; PROBABILISTIC RISK ASSESSMENT; UNCERTAINTY ANALYSIS; UNCERTAINTY IMPORTANCE MEASURES;

UNCERTAIN SYSTEMS;

EID: 33846350926     PISSN: 09518320     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ress.2006.04.015     Document Type: Article

References (50)

1. Apostolakis G.E. How useful is quantitative risk assessment. Risk Anal 24 3 (2005) 515-520
2. Hammit J.K., and Shiyakhter I. The expected value of information and the probability of surprise. Risk Anal 19 1 (1999) 135-152
3. Apostolakis G.E. A commentary on model uncertainty. Proceedings of the workshop on model uncertainty: its characterizaton and quantification (1995), Center for Reliability Engineering, University of Maryland, College Park, Maryland, USA
4. Saltelli A. Sensitivity analysis for importance assessment. Risk Anal 22 3 (2002) 579
5. Saltelli A., Tarantola S., and Campolongo F. Sensitivity analysis as an ingredient of modelling. Stat Sci 19 4 (2000) 377-395
6. Sobol IM. Sensitivity estimates for nonlinear mathematical models. Mat Model 1990;2(1):112-8 [in Russian]
7. Sobol IM. Sensitivity estimates for nonlinear mathematical models. MMCE 1993;1(4):407-14 [English translation].
8. Sobol' I.M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simulation 55 1 (2001) 271-280
9. Sobol' I.M. Theorem and examples on high-dimensional model representation. Reliab Eng Syst Saf 79 (2003) 187-193
10. Rabitz H., Alis O.F., Shorter J., and Shim K. Efficient input output model representation. Comput Phys Commun 117 (1998) 11-20
11. Rabitz H., and Alis O.F. General foundations of high-dimensional model representations. J Math Chem 25 2-3 (1999) 197-233
12. Alis O.F., and Rabitz H. Efficient computation of high dimensional model representations. J Math Chem 29 2 (2001) 127-142
13. Bedford T. Sensitivity indices for (tree)-dependent variables. Proceedings of the second international symposium on sensitivity analysis of model output (1998), Venice (Italy) 17-20
14. Tarantola S. Quantifying uncertainty importance when inputs are correlated. In: Cottam MP, Harvey DW, Pape RP, Tait J, editors, Foresight and precaution. Rotterdam: Balkema; 2000 [ISBN 9058091406].
15. Ratto M., Tarantola S., and Saltelli A. Estimating importance indicators for correlated inputs. Proceeding of ESREL 2001 (2001), Torino, Italy 157-164
16. Saltelli A., and Tarantola S. On the relative importance of input factors in mathematical models: safety assessment for nuclear waste disposal. J Am Stat Assoc 97 459 (2002) 702-709
17. Homma T., and Saltelli A. Use of Sobol' quasirandom sequence generator for integration of modified uncertainty importance measure. J Nucl Sci Technol 32 11 (1995) 1164-1173
18. Iman R.L., Johnson M.E., and Watson Jr. C.C. Uncertainty analysis for computer model projections of hurricane losses. Risk Anal 25 5 (2005) 1299-1312
19. Savage L. The foundations of statistics. 2nd ed. (1972), Dover Publications, Mineola, NY
20. Helton J.C., and Davis F.J. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf 81 (2003) 23-69
21. Chun M.-H., Han S.-J., and Tak N.-IL. An uncertainty importance measure using a distance metric for the change in a cumulative distribution function. Reliab Eng Syst Saf 70 (2000) 313-321
22. Nakashima K, Yamato K. Variance-importance of system components. IEEE Trans Reliab 1982;R-31(1).
23. Iman R.L., and Hora S.C. A robust measure of uncertainty importance for use in fault tree system analysis. Risk Anal 10 3 (1990) 401-406
24. Homma T., and Saltelli A. Importance measures in global sensitivity analysis of nonlinear models. Reliab Eng Syst Saf 52 (1996) 1-17
25. Iman R.L. A matrix-based approach to uncertainty and sensitivity analysis for fault trees. Risk Anal 7 1 (1987) 21-33
26. Iman R.L., and Conover W.J. A measure of top-down correlation. Technometrics 29 3 (1987) 351-357
27. Helton J.C. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliab Eng Syst Saf 42 (1993) 327-367
28. McKay MD. Variance-based methods for assessing uncertainty importance. In: NUREG-1150 analyses, LA-UR-96-2695; 1996, p. 1-27.
29. Saltelli A. The role of sensitivity analysis in the corroboration of models and its link to model structural and parametric uncertainty. Reliab Eng Syst Saf 57 (1997) 1-4
30. Saltelli A., and Marivoet J. Non-parametric statistics in sensitivity analysis for model output: a comparison of selected techniques. Reliab Eng Syst Saf 28 (1990) 229-253
31. Helton J.C., and Davis F.J. Sampling-based methods. In: Saltelli A., Chan K., and Scott E.M. (Eds). Sensitivity Analysis (2000), Wiley, New York 101-153
32. Hora SC, Iman RL. A comparison of maximus/bounding and Bayes/Monte Carlo for fault tree uncertainty analysis. Technical Report SAND85-2839, Sandia National Laboratories, Albuquerque, New Mexico; 1986.
33. Saltelli A., Tarantola S., and Chan K.P.-S. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41 1 (1999) 39-56
34. Park C.K., and Ahn K.I. A new approach for measuring uncertainty importance and distributional sensitivity in probabilistic safety assessment. Reliab Eng Syst Saf 46 (1994) 253-261
35. Bier V.M. A measure of uncertainty importance for components in fault trees. Trans Am Nucl Soc 45 1 (1983) 384-385
36. Borgonovo E., and Apostolakis G.E. A new importance measure for risk-informed decision-making. Reliab Eng Syst Saf 72 2 (2001) 193-212
37. Cheok M.C., Parry G.W., and Sherry R.R. Use of importance measures in risk-informed regulatory applications. Reliab Eng Syst Saf 60 (1998) 213-226
38. Borgonovo E., Apostolakis G.E., Tarantola S., and Saltelli A. Comparison of local and global sensitivity analysis techniques in probabilistic safety assessment. Reliab Eng Syst Saf 79 (2003) 175-185
39. Morris M.D. Factorial sampling plans for preliminary computational experiments. Technometrics 33 2 (1991) 161-174
40. Kleijnen P.C. An overview of the design and analysis of simulation experiments for sensitivity analysis. Eur J Oper Res 164 (2005) 287-300
41. Frey C.H., and Patil SR. Identification and review of sensitivity analysis methods. Risk Anal 22 3 (2002) 553-571
42. Hora S.C., and Helton J.C. A distribution-free test for the relationship between model input and output when using Latin hypercube sampling. Reliab Eng Syst Saf 79 (2003) 333-339
43. Oakley J.E., and O'Hagan A. Probabilistic sensitivity analysis of complex models: a Bayesian approach. J R Stat Soc: Ser B (Stat Methodol) 66 3 (2004) 751-769
44. Huang C.-F., and Litzenberger RH. Foundations for financial economics (1988), Prentice-Hall, Upper Saddle River, NJ, USA 365 [ISBN 0-13-500653-8]
45. Malliavin P. Integration and probability (1995), Springer, New York, USA [ISBN 0-387-94409-5]
46. Joint Research Center of the European Community. SIMLAB reference manual POLIS-JRC ISIS. Joint Research Center of the European Cummunity.
47. Campolongo F., and Saltelli A. Sensitivity analysis of an environmental model: an application of different analysis methods. Reliab Eng Syst Saf 57 (1997) 49-69
48. Patil S.R., and Frey C.H. Comparison of sensitivity analysis methods based on application to a food safety risk assessment model. Risk Anal 24 3 (2004) 573-585
49. Sobol I.M. Quasi-Monte Carlo methods. Prog Nucl Energy 24 (1990) 55-61
50. McKay M.D., Beckman R.J., and Conover W.J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21 2 (1979) 239-245