A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Simulation output analysis using standardized time series
Mathematics of Operations Research
An investigation of finite-sample behavior of confidence interval estimators
Operations Research
Asymptotic and finite-sample correlations between OBM estimators
WSC '93 Proceedings of the 25th conference on Winter simulation
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Principles of Discrete Event Simulation
Principles of Discrete Event Simulation
Overlapping batch means: something for nothing?
WSC '84 Proceedings of the 16th conference on Winter simulation
Advanced methods for simulation output analysis
WSC '95 Proceedings of the 27th 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
Implementing the batch means method in simulation experiments
WSC '96 Proceedings of the 28th conference on Winter simulation
Optimal quadratic-form estimator of the variance of the sample mean
Proceedings of the 29th conference on Winter simulation
VV&A; IV: validation of trace-driven simulation models: more on bootstrap tests
Proceedings of the 32nd conference on Winter simulation
Overlapping batch means: something more for nothing?
Proceedings of the Winter Simulation Conference
On the mean-squared error of variance estimators for computer simulations
Proceedings of the Winter Simulation Conference
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Batching is a well known technique for estimating the variance of point estimators computed from simulation experiments. The batch statistic variance estimator is simply the (appropriately scaled) sample variance of the estimator computed on subsets of data. The simulation and statistics communities seem to be largely unaware of each other's results in this area. Some empirical and theoretical results from the simulation and statistics literature will be discussed and compared. In particular, we discuss the important issue of selecting batch size and present a new data based method for determining it. The basic idea is to empirically estimate the optimal batch size for a smaller simulation length, and then extrapolate using knowledge of the optimal order of magnitude of batch length for the original simulation length. We provide a small simulation showing the effectiveness of the proposed method.