Multivariate statistical simulation
Multivariate statistical simulation
Error estimation in automatic quadrature routines
ACM Transactions on Mathematical Software (TOMS)
The impact of autocorrelation on queuing systems
Management Science
The TES methodology: modeling empirical stationary time series
WSC '92 Proceedings of the 24th conference on Winter simulation
Composition for multivariate random variables
WSC '94 Proceedings of the 26th conference on Winter simulation
Null rules and orthogonal expansions
Proceedings of the conference on Approximation and computation : a fetschrift in honor of Walter Gautschi: a fetschrift in honor of Walter Gautschi
Organ transplantation policy evaluation
WSC '95 Proceedings of the 27th conference on Winter simulation
Algorithm 764: Cubpack++: a C++ package for automatic two-dimensional cubature
ACM Transactions on Mathematical Software (TOMS)
Sensitivity of output performance measures to input distributions in queueing simulation modeling
Proceedings of the 29th conference on Winter simulation
Automatic modeling of file system workloads using two-level arrival processes
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Input modeling tools for complex problems
Proceedings of the 30th conference on Winter simulation
Correlations and Copulas for Decision and Risk Analysis
Management Science
Simulation Modeling and Analysis
Simulation Modeling and Analysis
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
Advanced input modeling: parameter estimation for ARTA processes
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
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
Clinic: correlated inputs in an automotive paint shop fire risk simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Analysis and generation of random vectors with copulas
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
MCDB: a monte carlo approach to managing uncertain data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Tools for dependent simulation input with copulas
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
Copula-Based Multivariate Input Models for Stochastic Simulation
Operations Research
Fitting discrete multivariate distributions with unbounded marginals and normal-copula dependence
Winter Simulation Conference
Simulating cointegrated time series
Winter Simulation Conference
The monte carlo database system: Stochastic analysis close to the data
ACM Transactions on Database Systems (TODS)
Correlated phase-type distributed random numbers as input models for simulations
Performance Evaluation
Data mining model-based control charts for multivariate and autocorrelated processes
Expert Systems with Applications: An International Journal
Simulating multivariate time series using flocking
Proceedings of the Winter Simulation Conference
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We present a model for representing stationary multivariate time-series input processes with marginal distributions from the Johnson translation system and an autocorrelation structure specified through some finite lag. We then describe how to generate data accurately to drive computer simulations. The central idea is to transform a Gaussian vector autoregressive process into the desired multivariate time-series input process that we presume as having a VARTA (Vector-Autoregressive-To-Anything) distribution. We manipulate the autocorrelation structure of the Gaussian vector autoregressive process so that we achieve the desired autocorrelation structure for the simulation input process. We call this the correlation-matching problem and solve it by an algorithm that incorporates a numerical-search procedure and a numerical-integration technique. An illustrative example is included.