Abstract
Estimating the variance of the sample mean is a classical problem of steady-state simulation output analysis. Traditional batch means estimators require specification of the simulation run length a priori. To our knowledge, the Dynamic Non-overlapping Batch Means (DNBM) estimator is the only existing variance estimator that requires a constant storage space for any sample size. In this paper, we develop the Dynamic Partial-overlapping Batch Means (DPBM) algorithm, that also requires a constant storage space. In terms of the mean squared error, the statistical performance of the DPBM estimators is superior to that of the DNBM estimators.
Acknowledgements
This research is supported by the National Science Council of the Republic of China under grant NSC-95-2221-E-007-175. The author thanks David Goldsman for his comments. The author also thanks Tsu-Kuang Yang and Ming-Chang Chih for interesting discussions and help with the flowchart of the DPBM algorithm.