Multivariate statistical simulation
Multivariate statistical simulation
Generating random deviates from multivariate Pearson distributions
Computational Statistics & Data Analysis
A rejection technique for sampling from T-concave distributions
ACM Transactions on Mathematical Software (TOMS)
Multivariate input modeling with Johnson distributions
WSC '96 Proceedings of the 28th conference on Winter simulation
A rejection technique for sampling from log-concave multivariate distributions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Correlations and Copulas for Decision and Risk Analysis
Management Science
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations
INFORMS Journal on Computing
Generating negatively correlated gamma variates using the Beta-Gamma transformation
WSC '83 Proceedings of the 15th conference on Winter simulation - Volume 1
Behavior of the NORTA method for correlated random vector generation as the dimension increases
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Asymmetric variate generation via a parameterless dual neural learning algorithm
Computational Intelligence and Neuroscience - Processing of Brain Signals by Using Hemodynamic and Neuroelectromagnetic Modalities
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Growing technology, escalating capability, and increasing complexity in many real world systems demand the applications of multivariate statistical analysis approaches by simulation. In these approaches, generating multivariate random vectors is a crucial part of the system modeling and analyzing. The NORTA algorithm, in which generating the correlation matrices of normal random vectors is the most important task, is one of the most efficient methods in this area. To do this, we need to solve some complicated equations. Many researchers have tried to solve these equations by three general approaches of (1) solving nonlinear equations analytically, (2) solving equations numerically, and (3) solving equations by simulation. In this paper, we develop a new method to generate the correlation matrices of normal random vectors based on the artificial neural networks approach. We apply the Perseptron Neural Network as the best applicable network to function fitting. In order to understand the proposed method better, we present two numerical examples and report the results of simulation studies.