Multivariate statistical simulation
Multivariate statistical simulation
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
Advanced input modeling: properties of the NORTA method in higher dimensions
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
ACM Transactions on Modeling and Computer Simulation (TOMACS)
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
A method for fast generation of bivariate Poisson random vectors
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Generating spike trains with specified correlation coefficients
Neural Computation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Generating random correlation matrices based on vines and extended onion method
Journal of Multivariate Analysis
Polynomial chaos representation of spatio-temporal random fields from experimental measurements
Journal of Computational Physics
An Algorithm for Fast Generation of Bivariate Poisson Random Vectors
INFORMS Journal on Computing
Generating random AR(p) and MA(q) Toeplitz correlation matrices
Journal of Multivariate Analysis
Fitting a normal copula for a multivariate distribution with both discrete and continuous marginals
Winter Simulation Conference
Fitting discrete multivariate distributions with unbounded marginals and normal-copula dependence
Winter Simulation Conference
C-NORTA: A Rejection Procedure for Sampling from the Tail of Bivariate NORTA Distributions
INFORMS Journal on Computing
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The NORTA method is a fast general-purpose method for generating samples of a random vector with given marginal distributions and given correlation matrix. It is known that there exist marginal distributions and correlation matrices that the NORTA method cannot match, even though a random vector with the prescribed qualities exists. We investigate this problem as the dimension of the random vector increases. Simulation results show that the problem rapidly becomes acute, in the sense that NORTA fails to work with an increasingly large proportion of correlation matrices. Simulation results also show that if one is willing to settle for a correlation matrix that is "close" to the desired one, then NORTA performs well with increasing dimension. As part of our analysis, we develop a method for sampling correlation matrices uniformly (in a certain precise sense) from the set of all such matrices. This procedure can be used more generally for sampling uniformly from the space of all symmetric positive definite matrices with diagonal elements fixed at positive values.