On modeling correlated random variables in risk assessment.
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.
Haas
C N
CN
School of Environmental Science, Engineering & Policy, Drexel University, Philadelphia, PA 19104, USA.
D000465:Algorithms; D001572:Benzofurans / Q000009:adverse effects; D004147:Dioxins / Q000009:adverse effects; D004785:Environmental Pollutants / Q000009:adverse effects; D006256:Hazardous Waste / Q000009:adverse effects; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D018570:Risk Assessment; D012984:Software
D001572:Benzofurans; D004147:Dioxins; D004785:Environmental Pollutants; D006256:Hazardous Waste