Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Principles of Discrete Event Simulation
Principles of Discrete Event Simulation
Interactive analysis of output from GPSS-based simulations
WSC '78 Proceedings of the 10th conference on Winter simulation - Volume 1
Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
Advanced output analysis for simulation
WSC '92 Proceedings of the 24th conference on Winter simulation
WSC '85 Proceedings of the 17th 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
Computational experience with the batch means method
Proceedings of the 29th conference on Winter simulation
Advanced methods for simulation output analysi8
Proceedings of the 30th conference on Winter simulation
Output analysis for simulation
WSC '91 Proceedings of the 23rd conference on Winter simulation
WSC '86 Proceedings of the 18th conference on Winter simulation
Automated estimation and variance reduction for steady-state simulations
WSC '86 Proceedings of the 18th conference on Winter simulation
Output analysis for simulation (tutorial session)
WSC' 90 Proceedings of the 22nd conference on Winter simulation
A conceptual framework for research in the analysis of simulation output
Communications of the ACM - Special issue on simulation modeling and statistical computing
Output analysis: output analysis for simulations
Proceedings of the 32nd conference on Winter simulation
Output analysis: output data analysis for simulations
Proceedings of the 33nd conference on Winter simulation
WSC '83 Proceedings of the 15th conference on Winter simulation - Volume 1
WSC '81 Proceedings of the 13th conference on Winter simulation - Volume 1
WSC '84 Proceedings of the 16th conference on Winter simulation
Overlapping batch means: something for nothing?
WSC '84 Proceedings of the 16th conference on Winter simulation
Statistical analysis of simulation output: output data analysis for simulations
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
WSC '82 Proceedings of the 14th conference on Winter Simulation - Volume 2
Overlapping batch means: something for nothing? (1984)
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Properties of batch means from stationary ARMA time series
Operations Research Letters
Thirty years of "batch size effects"
Proceedings of the Winter Simulation Conference
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An interactive FORTRAN subroutine is presented for use with ongoing simulations to determine and collect the sample size needed to estimate the mean of a process with a specified level of statistical precision. The subroutine can be used with simulation models written in a variety of languages, e.g., FORTRAN, GASP, GPSS, SIMSCRIPT. The subroutine partitions a sequence of observations on the random variable of interest into a series of consecutive batches, finding those batch sizes whose batch means are independent. The classical iid method is then applied to build a confidence interval on the mean. Under interactive user control, the subroutine then goes back to the simulation model as often as may be necessary to extend sample size to the point that the confidence interval satisfies the user's needs. This paper complements an earlier paper presenting software for interactive autoregressive analysis of simulation output [1]. The present paper reports on the use of both techniques to analyze data produced by data models for which analytic results are known. The method of batch means is not successful in identifying the batch size for which the batch means are known to be independent in one of these data sets. This raises serious questions about the procedure used to test for independence of batch means, and points out the need for further research in this area.