Input modeling tools for complex problems
Proceedings of the 30th conference on Winter simulation
Proceedings of the 33nd conference on Winter simulation
A Heuristic for Moment-Matching Scenario Generation
Computational Optimization and Applications
Computers and Operations Research
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Behavior of the NORTA method for correlated random vector generation as the dimension increases
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Advanced input modeling: properties of the NORTA method in higher dimensions
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Dependence modeling for stochastic simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
Generating scenario trees: A parallel integrated simulation-optimization approach
Journal of Computational and Applied Mathematics
A review of scenario generation methods
International Journal of Computing Science and Mathematics
Characterization of BitTorrent swarms and their distribution in the Internet
Computer Networks: The International Journal of Computer and Telecommunications Networking
Expert Systems with Applications: An International Journal
Fitting a normal copula for a multivariate distribution with both discrete and continuous marginals
Winter Simulation Conference
A new moment matching algorithm for sampling from partially specified symmetric distributions
Operations Research Letters
Scenario construction and reduction applied to stochastic power generation expansion planning
Computers and Operations Research
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This paper presents an algorithm for generating correlated vectors of random numbers. The user need not fully specify the joint distribution function; instead, the user "partially specifies" only the marginal distributions and the correlation matrix. The algorithm may be applied to any set of continuous, strictly increasing distribution functions; the marginal distributions need not all be of the same functional form. The correlation matrix is first checked for mathematical consistency (positive semi and adjusted if necessary. Then the correlated random vectors are generated using a combination of Cholesky decomposition and Gauss-Newton iteration. Applications are made to cost analysis, where correlations are often present between cost elements in a work breakdown structure.