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Correlated Inputs in Quantitative Risk Assessment: The Effects of Distributional Shape

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  • John Bukowski
  • Leo Korn
  • Daniel Wartenberg

Abstract

Application of Monte Carlo simulation methods to quantitative risk assessment are becoming increasingly popular. With this methodology, investigators have become concerned about correlations among input variables which might affect the resulting distribution of risk. We show that the choice of input distributions in these simulations likely has a larger effect on the resultant risk distribution than does the inclusion or exclusion of correlations. Previous investigators have studied the effect of correlated input variables for the addition of variables with any underlying distribution and for the product of lognormally distributed variables. The effects in the main part of the distribution are small unless the correlation and variances are large. We extend this work by considering addition, multiplication and division of two variables with assumed normal, lognormal, uniform and triangular distributions. For all possible pairwise combinations, we find that the effects of correlated input variables are similar to those observed for lognormal distributions, and thus relatively small overall. The effect of using different distributions, however, can be large.

Suggested Citation

  • John Bukowski & Leo Korn & Daniel Wartenberg, 1995. "Correlated Inputs in Quantitative Risk Assessment: The Effects of Distributional Shape," Risk Analysis, John Wiley & Sons, vol. 15(2), pages 215-219, April.
    • Handle: RePEc:wly:riskan:v:15:y:1995:i:2:p:215-219
    DOI: 10.1111/j.1539-6924.1995.tb00315.x
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    References listed on IDEAS

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    1. Andrew E. Smith & P. Barry Ryan & John S. Evans, 1992. "The Effect of Neglecting Correlations When Propagating Uncertainty and Estimating the Population Distribution of Risk," Risk Analysis, John Wiley & Sons, vol. 12(4), pages 467-474, December.
    2. Kimberly M. Thompson & David E. Burmaster & Edmund A.C. Crouch3, 1992. "Monte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 12(1), pages 53-63, March.
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    Cited by:

    1. Per Sander & Bo Bergbäck & Tomas Öberg, 2006. "Uncertain Numbers and Uncertainty in the Selection of Input Distributions—Consequences for a Probabilistic Risk Assessment of Contaminated Land," Risk Analysis, John Wiley & Sons, vol. 26(5), pages 1363-1375, October.
    2. Maged M. Hamed & Philip B. Bedient, 1997. "On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 17(1), pages 97-105, February.
    3. Nguyen, Tristan & Molinari, Robert Danilo, 2009. "Quantifizierung von Abhängigkeitsstrukturen zwischen Risiken in Versicherungsunternehmen," German Risk and Insurance Review (GRIR), University of Cologne, Department of Risk Management and Insurance, vol. 5(2), pages 28-52.
    4. Hildebrandt, Patrick & Knoke, Thomas, 2011. "Investment decisions under uncertainty--A methodological review on forest science studies," Forest Policy and Economics, Elsevier, vol. 13(1), pages 1-15, January.
    5. Charles N. Haas, 1999. "On Modeling Correlated Random Variables in Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 19(6), pages 1205-1214, December.

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