On the relationship between batch means, overlapping means and spectral estimation
WSC '87 Proceedings of the 19th conference on Winter simulation
Large and small sample comparisons of various variance estimators
WSC '86 Proceedings of the 18th conference on Winter simulation
A spectral method for confidence interval generation and run length control in simulations
Communications of the ACM - Special issue on simulation modeling and statistical computing
An Introduction to the Regenerative Method for Simulation Analysis
An Introduction to the Regenerative Method for Simulation Analysis
Overlapping batch means: something for nothing?
WSC '84 Proceedings of the 16th conference on Winter simulation
Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
Batching methods in simulation output analysis: what we know and what we don't
WSC '96 Proceedings of the 28th conference on Winter simulation
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Simulation output analysis: a tutorial based on one research thread
WSC '04 Proceedings of the 36th conference on Winter simulation
Operations Research Letters
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Various types of estimators have been proposed for estimating the variance of the sample mean, a fundamental quantity in simulation output analysis. When used with low degrees of freedom, several of these estimators have little bias. But the low degrees of freedom correspond to high variance. One approach to creating estimators with smaller variance while maintaining the negligible bias is to use linear combinations of known estimators. Whether linear combinations provide improved estimators — and, if so, the choice of estimators to be included in the linear combination — depends upon the correlations among the various estimators. Linear combinations of estimators having high positive correlation would provide little improvement while combinations of independent estimators would provide substantial gain. We investigate the correlation among four well-known estimators as a function of the type of stochastic process generating the data, the sample size, the estimator type, and estimator parameters.