Automated estimation and variance reduction via control variates for infinite-horizon simulations
Computers and Operations Research
Multivariate inference in stationary simulation using batch means
WSC '87 Proceedings of the 19th conference on Winter simulation
Control of initialization bias in multivariate simulation response
Communications of the ACM - Special issue on simulation modeling and statistical computing
Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
A sequential procedure for simultaneous estimation of several means
ACM Transactions on Modeling and Computer Simulation (TOMACS)
WSC '95 Proceedings of the 27th conference on Winter simulation
Statistical analysis of output processes
WSC '93 Proceedings of the 25th conference on Winter simulation
Batching methods in simulation output analysis: what we know and what we don't
WSC '96 Proceedings of the 28th conference on Winter simulation
Multivariate simulation output analysis
WSC '91 Proceedings of the 23rd conference on Winter simulation
Power comparisons for the multivariate batch-means method
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Batch-size effects on simulation optimization using multiple comparisons with the best
WSC' 90 Proceedings of the 22nd conference on Winter simulation
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Research on the analysis of steady-state simulation experiments has concentrated on mitigating the effects of initial-condition bias and estimating the variance of the simulation point estimator, usually a sample mean. There has been little research on improving the precision of point estimators through variance reduction, especially in multivariate estimation problems. In fact, multivariate estimation procedures are rarely used in simulation output analysis.We consider applying the non-overlaping batch means output analysis method in conjunction with the control-variates variance reduction technique to estimate a multivariate mean vector. The effect of the number of batches and the number of control variates on the multivariate point and region estimators and the univariate point and interval estimators are considered. Our results have implications for terminating simulations as well.