Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines
Annals of Mathematics and Artificial Intelligence
Numerical Methods for Fitting and Simulating Autoregressive-To-Anything Processes
INFORMS Journal on Computing
Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations
INFORMS Journal on Computing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Fitting Time-Series Input Processes for Simulation
Operations Research
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Evaluation of the ARTAFIT Method for Fitting Time-Series Input Processes for Simulation
INFORMS Journal on Computing
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
Correlated phase-type distributed random numbers as input models for simulations
Performance Evaluation
A Copulas-Based Approach to Modeling Dependence in Decision Trees
Operations Research
Simulating multivariate time series using flocking
Proceedings of the Winter Simulation Conference
Data-driven simulation of complex multidimensional time series
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
| Hi-index | 0.01 |
As large-scale discrete-event stochastic simulation becomes a tool that is used routinely for the design and analysis of stochastic systems, the need for input-modeling support with the ability to represent complex interactions and interdependencies among the components of multivariate time-series input processes is more critical than ever. Motivated by the failure of independent and identically distributed random variables to represent such input processes, a comprehensive framework called Vector-Autoregressive-To-Anything (VARTA) has been introduced for multivariate time-series input modeling. Despite its flexibility in capturing a wide variety of distributional shapes, we show that VARTA falls short in representing dependence structures that arise in situations where extreme component realizations occur together. We demonstrate that it is possible to extend VARTA to work for such dependence structures via the use of the copula theory, which has been used primarily for random vectors in the simulation input-modeling literature, for multivariate time-series input modeling. We show that our copula-based multivariate time-series input model, which includes VARTA as a special case, allows the development of statistically valid fitting and fast sampling algorithms well suited for driving large-scale stochastic simulations.