ACM Transactions on Modeling and Computer Simulation (TOMACS)
Advanced input modeling: parameter estimation for ARTA processes
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
Copula-Based Multivariate Input Models for Stochastic Simulation
Operations Research
An Algorithm for Fast Generation of Bivariate Poisson Random Vectors
INFORMS Journal on Computing
C-NORTA: A Rejection Procedure for Sampling from the Tail of Bivariate NORTA Distributions
INFORMS Journal on Computing
On generating multivariate Poisson data in management science applications
Applied Stochastic Models in Business and Industry
A Copulas-Based Approach to Modeling Dependence in Decision Trees
Operations Research
| Hi-index | 0.00 |
We propose a specific method for generatingn-dimensional random vectors with given marginal distributions and correlation matrix. The method uses the NORTA (NORmal To Anything) approach, which generates a standard normal random vector and then transforms it into a random vector with specified marginal distributions. During initialization,n( n-1)/2 nonlinear equations need to be solved to ensure that the generated random vector has the specified correlation structure. To solve these equations, we apply retrospective approximation, a generic stochastic root-finding algorithm, with slight changes. Internal control variates are used to estimate function values. Empirical comparisons show that the control-variate variance-reduction technique improves the algorithm's convergence speed as well as its robustness. Simulation results for a variety of marginal distributions and correlation matrices are also presented.