A spectral method for confidence interval generation and run length control in simulations
Communications of the ACM - Special issue on simulation modeling and statistical computing
Applied Wavelet Analysis with S-Plus
Applied Wavelet Analysis with S-Plus
Output analysis: ASAP2: an improved batch means procedure for simulation output analysis
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
Experimental performance evaluation of histogram approximation for simulation output analysis
WSC '04 Proceedings of the 36th conference on Winter simulation
Exploring exponentially weighted moving average control charts to determine the warm-up period
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
Performance evaluation of spectral procedures for simulation analysis
Proceedings of the 38th conference on Winter simulation
SBatch: a spaced batch means procedure for simulation analysis
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Automating warm-up length estimation
Proceedings of the 40th Conference on Winter Simulation
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We develop an automated wavelet-based spectral method for constructing an approximate confidence interval on the steady-state mean of a simulation output process. This procedure, called WASSP, determines a batch size and a warm-up period beyond which the computed batch means form an approximately stationary Gaussian process. Based on the log-smoothed-periodogram of the batch means, WASSP uses wavelets to estimate the batch means log-spectrum and ultimately the steady-state variance constant (SSVC) of the original (unbatched) process. WASSP combines the SSVC estimator with the grand average of the batch means in a sequential procedure for constructing a confidence-interval estimator of the steady-state mean that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. An extensive performance evaluation provides evidence of WASSP's robustness in comparison with some other output analysis methods.