Simulation Modeling and Analysis
Simulation Modeling and Analysis
Traffic Modeling of IP Networks Using the Batch Markovian Arrival Process
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
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
The accuracy of a new confidence interval method
WSC '04 Proceedings of the 36th conference on Winter simulation
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Analysis and generation of random vectors with copulas
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
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Copulas encompass the entire dependence structure of multivariate distributions, and not only the correlations. Together with the marginal distributions of the vector elements, they define a multivariate distribution which can be used to generate random vectors with this distribution. A toolbox is presented which implements input models with this method, for random vectors and time series. Time series are modeled with some general autoregressive processes. The copulas are estimated from observed samples of random vectors. The MATLAB tool calculates the copula, generates random vectors and time series, and provides statistics and diagrams which indicate validity and accuracy of the input model. It is fast and allows for random vectors with high dimensions, for example 100. For this efficiency an intricate data structure is essential. The generation algorithm is also implemented with Java methods.