Journal article
On Modeling Correlated Random Variables in Risk Assessment
Risk analysis, v 19(6), pp 1205-1214
Dec 1999
PMID: 10765457
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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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Details
- Title
- On Modeling Correlated Random Variables in Risk Assessment
- Creators
- Charles Haas - School of Environmental Science, Engineering & Policy, Drexel University Philadelphia PA 19104
- Publication Details
- Risk analysis, v 19(6), pp 1205-1214
- Publisher
- Kluwer Academic Publishers-Plenum Publishers; New York
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000084295700015
- Scopus ID
- 2-s2.0-0033405246
- Other Identifier
- 991014878614504721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Mathematics, Interdisciplinary Applications
- Public, Environmental & Occupational Health
- Social Sciences, Mathematical Methods