Abstract
ABSTRACT Estimating the variance of the sample mean is a classical problem of stochastic simulation. Traditional batch means estimators require specification of the simulation run length a priori. To our knowledge, the dynamic non-overlapping batch means estimator (DNBM) and dynamic partial-overlapping batch means estimator (DPBM) are the only two existing variance estimators requiring a constant storage space for any sample size. The performance of the DPBM is better than that of DNBM in terms of the mse criteria, but the DPBM requires four times more memory than the DNBM. This paper improves the DPBM by developing a computational version of the DPBM that requires the same storage space as the DNBM estimator.
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