Mathematical statistics (4th ed.)
Mathematical statistics (4th ed.)
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
LABATCH.2: software for statistical analysis of simulation sample path data
Proceedings of the 30th conference on Winter simulation
Discrete-event simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
On the MSE robustness of batching estimators
Proceedings of the 33nd conference on Winter simulation
On the MSE robustness of batching estimators
Proceedings of the 33nd conference on Winter simulation
Convergence Properties of the Batch Means Method for Simulation Output Analysis
INFORMS Journal on Computing
Overlapping batch means: something for nothing?
WSC '84 Proceedings of the 16th conference on Winter simulation
An Improved Batch Means Procedure for Simulation Output Analysis
Management Science
Output analysis: on choosing a single criterion for confidence-interval procedures
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Steady-state simulation analysis using ASAP3
WSC '04 Proceedings of the 36th conference on Winter simulation
Review of advanced methods for simulation output analysis
WSC '05 Proceedings of the 37th conference on Winter simulation
Performance evaluation of ASAP3 for steady-state output analysis
WSC '05 Proceedings of the 37th conference on Winter simulation
Replicated batch means variance estimators in the presence of an initial transient
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A comprehensive review of methods for simulation output analysis
Proceedings of the 38th conference on Winter simulation
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Statistical analysis of simulation output: state of the art
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Replicated batch means for steady-state simulations with initial transients
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Overlapping batch means: something more for nothing?
Proceedings of the Winter Simulation Conference
Statistical issues in ad hoc distributed simulations
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Steady-State Simulation with Replication-Dependent Initial Transients: Analysis and Examples
INFORMS Journal on Computing
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When designing steady-state computer simulation experiments, one may be faced with the choice of batching observations in one long run or replicating a number of smaller runs. Both methods are potentially useful in the course of undertaking simulation output analysis. The tradeoffs between the two alternatives are well known: batching ameliorates the effects of initialization bias, but produces batch means that might be correlated; replication yields independent sample means, but may suffer from initialization bias at the beginning of each of the runs. We present several new results and specific examples to lend insight as to when one method might be preferred over the other. In steady-state, batching and replication perform similarly in terms of estimating the mean and variance parameter, but replication tends to do better than batching with regard to the performance of confidence intervals for the mean. Such a victory for replication may be hollow, for in the presence of an initial transient, batching often performs better than replication when it comes to point and confidence-interval estimation of the steady-state mean. We conclude---like other classic references---that in the context of estimation of the steady-state mean, batching is typically the wiser approach.