Generating "dependent" quasi-random numbers
Proceedings of the 32nd conference on Winter simulation
Proceedings of the 33nd conference on Winter simulation
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
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
Computing worst-case tail probabilities in credit risk
Proceedings of the 38th conference on Winter simulation
Tools for dependent simulation input with copulas
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
Polynomial chaos representation of spatio-temporal random fields from experimental measurements
Journal of Computational Physics
Simulating cointegrated time series
Winter Simulation Conference
A Copulas-Based Approach to Modeling Dependence in Decision Trees
Operations Research
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There is a growing need for the ability to specify and generate correlated random variables as primitive inputs to stochastic models.Moti vated by this need, several authors have explored the generation of random vectors with specified marginals, together with a specified covariance matrix, through the use of a transformation of a multivariate normal random vector (the NORTA method).A covariance matrix is said to be feasible for a given set of marginal distributions if a random vector exists with these characteristics. We develop a computational approach for establishing whether a given covariance matrix is feasible for a given set of marginals. The approach is used to rigorously establish that there are sets of marginals with feasible covariance matrix that the NORTA method cannot match. In such cases, we show how to modify the initialization phase of NORTA so that it will exactly match the marginals, and approximately match the desired covariance matrix.An important feature of our analysis is that we show that for almost any covariance matrix (in a certain precise sense), our computational procedure either explicitly provides a construction of a random vector with the required properties, or establishes that no such random vector exists.