Discrete event simulations and parallel processing: statistical properties
SIAM Journal on Scientific and Statistical Computing
Analysis of parallel replicated simulations under a completion time constraint
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Analysis if initial transient deletion for parallel steady-state simulations
SIAM Journal on Scientific and Statistical Computing
Evaluation of tests for initial-condition bias
WSC '92 Proceedings of the 24th conference on Winter simulation
Some new results on the initial transient problem
WSC '95 Proceedings of the 27th conference on Winter simulation
A comparison of five steady-state truncation heuristics for simulation
Proceedings of the 32nd conference on Winter simulation
Empirical performance of bias-reducing estimators for regenerative steady-state simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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We propose a new procedure for building confidence interval estimators of steady-state parameters in discrete event simulations. The procedure uses parallel processors to generate independent replications and constructs the confidence interval estimator by solving a generalized least square problem. The most appealing theoretical feature of the proposed procedure is that the precision of the resulted estimator can be improved by simply increasing the number of processors (or independent replications) while the simulated time length is fixed on an appropriate level on each processor. Experiments conducted on M/M/1 queue waiting time processes in heavy traffic confirm this theoretical property.