Correlations and Copulas for Decision and Risk Analysis
Management Science
Generating "dependent" quasi-random numbers
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation mathematics and random number generation: mathematics for simulation
Proceedings of the 33nd conference on Winter simulation
Proceedings of the 33nd conference on Winter simulation
Behavior of the NORTA method for correlated random vector generation as the dimension increases
ACM Transactions on Modeling and Computer Simulation (TOMACS)
IEEE Transactions on Mobile Computing
Analysis and generation of random vectors with copulas
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Asymmetric variate generation via a parameterless dual neural learning algorithm
Computational Intelligence and Neuroscience - Processing of Brain Signals by Using Hemodynamic and Neuroelectromagnetic Modalities
Tools for dependent simulation input with copulas
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
| Hi-index | 0.00 |
The NORTA method for multivariate generation is a fast general purpose method for generating samples of a random vector with given marginal distributions and given product-moment or rank correlation matrix. However, this method has been shown to fail to work for some feasible correlation matrices. (A matrix is feasible if there exists a random vector with the given marginal distributions and the matrix as the correlation matrix.) We investigate how this feasibility problem behaves as the dimension of the random vector is increased and find the problem to become acute rapidly. We also find that a modified NORTA procedure, augmented by a semidefinite program (SDP) that aims to generate a correlation matrix "close" to the desired one, performs well with increasing dimension.