Simulation output analysis using standardized time series
Mathematics of Operations Research
An investigation of finite-sample behavior of confidence interval estimators
Operations Research
Optimal mean-squared-error batch sizes
Management Science
Large-sample results for batch means
Management Science
Discrete-event simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
A data warehouse environment for storing and analyzing simulation output data
WSC '04 Proceedings of the 36th conference on Winter simulation
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When designing steady-state computer simulation experiments, one is often faced with the choice of batching observations in one long run or replicating a number of smaller runs. Both methods are potentially useful in simulation output analysis. In its simplest form, the choice boils down to: Should we divide one long run into b adjacent, nonoverlapping batches, each of size m? Or should we conduct b independent replications, each of length m? We give results and examples to lend insight as to when one method might be preferred over the other. In the steady state case, batching and replication perform about the same in terms of estimating the mean and variance parameter, though replication tends to do better than batching when it comes to the performance of confidence intervals for the mean. On the other hand, batching can often do better than replication when it comes to point and confidence-interval estimation of the steady-state mean in the presence of an initial transient. This is not particularly surprising, and is a common rule ofthumb in the folklore.