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Importance of Distributional Form in Characterizing Inputs to Monte Carlo Risk Assessments

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  • Charles N. Haas

Abstract

The selection among distributional forms for inputs into uncertainty and variability (e.g., Monte Carlo) analyses is an important task. This paper considers the importance of distributional selection by examining the overall and tail behavior of the lognormal, Weibull, gamma, and inverse gaussian distributions. It is concluded that at low relative standard deviation (below 1), there is less of a difference between upper tail behavior among the distributions than at higher RSD values. Sample sizes in excess of 200 are required to reliably distinguish between distributional forms at the higher RSD values. The likelihood statistic appears to offer a reasonable approach to distributional discrimination, and it, or a similar approach, should be incorporated into distributional fitting procedures used in risk analysis.

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  • Charles N. Haas, 1997. "Importance of Distributional Form in Characterizing Inputs to Monte Carlo Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 17(1), pages 107-113, February.
    • Handle: RePEc:wly:riskan:v:17:y:1997:i:1:p:107-113
    DOI: 10.1111/j.1539-6924.1997.tb00849.x
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    2. Martí Nadal & Vikas Kumar & Marta Schuhmacher & José L. Domingo, 2008. "Applicability of a Neuroprobabilistic Integral Risk Index for the Environmental Management of Polluted Areas: A Case Study," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 271-286, April.
    3. Freeman, Jade & Modarres, Reza, 2006. "Inverse Box-Cox: The power-normal distribution," Statistics & Probability Letters, Elsevier, vol. 76(8), pages 764-772, April.
    4. Modarres, Reza & Nayak, Tapan K. & Gastwirth, Joseph L., 2002. "Estimation of upper quantiles under model and parameter uncertainty," Computational Statistics & Data Analysis, Elsevier, vol. 39(4), pages 529-554, June.
    5. Uddameri, Venkatesh & Ghaseminejad, Ali & Hernandez, E. Annette, 2020. "A tiered stochastic framework for assessing crop yield loss risks due to water scarcity under different uncertainty levels," Agricultural Water Management, Elsevier, vol. 238(C).
    6. 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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