Risk Analysis

, Volume 19, Issue 6, pp 1205–1214

On Modeling Correlated Random Variables in Risk Assessment

  • Charles N. Haas

DOI: 10.1023/A:1007047014741

Cite this article as:
Haas, C.N. Risk Anal (1999) 19: 1205. doi:10.1023/A:1007047014741


Monte Carlo methods in risk assessment are finding increasingly widespread application. With the recognition that inputs may be correlated, the incorporation of such correlations into the simulation has become important. Most implementations rely upon the method of Iman and Conover for generating correlated random variables. In this work, alternative methods using copulas are presented for deriving correlated random variables. It is further shown that the particular algorithm or assumption used may have a substantial effect on the output results, due to differences in higher order bivariate moments.

Monte Carlocorrelationcopulasbivariate distributionsdioxins

Copyright information

© Society for Risk Analysis 1999

Authors and Affiliations

  • Charles N. Haas
    • 1
  1. 1.School of Environmental Science, Engineering & Policy, Drexel UniversityPhiladelphia