LABATCH.2: software for statistical analysis of simulation sample path data
Proceedings of the 30th conference on Winter simulation
Improved batching for confidence interval construction in steady-state simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Experimental performance evaluation of batch means procedures for simulation output analysis
Proceedings of the 32nd conference on Winter simulation
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Convergence Properties of the Batch Means Method for Simulation Output Analysis
INFORMS Journal on Computing
An Improved Batch Means Procedure for Simulation Output Analysis
Management Science
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Output analysis: ASAP2: an improved batch means procedure for simulation output analysis
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation output analysis: a wavelet-based spectral method for steady-state simulation analysis
Proceedings of the 35th conference on Winter simulation: driving innovation
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Performance evaluation of ASAP3 for steady-state output analysis
WSC '05 Proceedings of the 37th 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
Winter Simulation Conference
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We discuss ASAP3, a refinement of the batch means algorithms ASAP and ASAP2. ASAP3 is a sequential procedure designed to produce a confidence-interval estimator for the expected response of a steady-state simulation that satisfies user-specified precision and coverage-probability requirements. ASAP3 operates as follows: the batch size is increased until the batch means pass the Shapiro-Wilk test for multivariate normality; and then ASAP3 fits a first-order autoregressive (AR(1)) time series model to the batch means. If necessary, the batch size is further increased until the autoregressive parameter in the AR(1) model does not significantly exceed 0.8. Next ASAP3 computes the terms of an inverse Cornish-Fisher expansion for the classical batch means t-ratio based on the AR(1) parameter estimates; and finally ASAP3 delivers a correlation-adjusted confidence interval based on this expansion. ASAP3 compared favorably with other batch means procedures (namely, ABATCH, ASAP, ASAP2, and LBATCH) in an extensive experimental performance evaluation.