Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 192, Issue 2, Pages 468-478Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2007.09.024
Keywords
Simulation; Regression; Stochastic processes; Statistical dependence; Correlation
Funding
- National Scientific and Engineering Research Council (NSERC) of Canada
- Golder Associated Ltd. of Calgary, Alberta
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Simulation has gained acceptance in the operations research community as a viable method for analyzing complex problems. While random generation of variables with various marginal distributions has been studied at length, developing ability to preserve a given degree of statistical dependence among them has been lagging behind. This paper includes a short summary of the previous work and a description of the proposed algorithm for efficient re-arranging of generated random variables such that a desired product moment correlation matrix is induced. The proposed approach is different from similar algorithms that induce a desired rank-order correlation among random variables. The algorithm is demonstrated using three numerical examples, one of which also includes a comparison with @RISK commercial package. Its main features are simplicity, ease of implementation and the ability to handle either theoretical or empirical distribution functions. (C) 2007 Elsevier B.V. All rights reserved.
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