On Modeling Correlated Random Variables in Risk Assessment
- Charles N. Haas
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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.
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- On Modeling Correlated Random Variables in Risk Assessment
Volume 19, Issue 6 , pp 1205-1214
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers-Plenum Publishers
- Additional Links
- Monte Carlo
- bivariate distributions
- Charles N. Haas (1)
- Author Affiliations
- 1. School of Environmental Science, Engineering & Policy, Drexel University, Philadelphia, PA, 19104