Experimental performance evaluation of batch means procedures for simulation output analysis
Proceedings of the 32nd conference on Winter simulation
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
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
Performance evaluation of ASAP3 for steady-state output analysis
WSC '05 Proceedings of the 37th conference on Winter simulation
Skart: a skewness-and autoregression-adjusted batch-means procedure for simulation analysis
Proceedings of the 40th Conference on Winter Simulation
INFORMS Journal on Computing
Analysis of sequential stopping rules
Winter Simulation Conference
Finite-Sample Performance of Absolute Precision Stopping Rules
INFORMS Journal on Computing
INFORMS Journal on Computing
Variance estimation and sequential stopping in steady-state simulations using linear regression
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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We formulate and evaluate the Automated Simulation Analysis Procedure (ASAP), an algorithm for steady-state simulation output analysis based on the method of nonover-lapping batch means (NOBM). ASAP delivers a confidence interval for an expected response that is centered on the sample mean of a portion of a simulation-generated time series and satisfies a user-specified absolute or relative precision requirement. ASAP operates as follows: The batch size is progressively increased until either (a) the batch means pass the von Neumann test for independence, and then ASAP delivers a classical NOBM confidence interval; or (b) the batch means pass the Shapiro-Wilk test for multivariate normality, and then ASAP delivers a correlation-adjusted confidence interval. The latter adjustment is based on an inverted Cornish-Fisher expansion for the classical NOBMt-ratio, where the terms of the expansion are estimated via an autoregressive-moving average time series model of the batch means. After determining the batch size and confidence-interval type, ASAP sequentially increases the number of batches until the precision requirement is satisfied. An extensive experimental study demonstrates the performance improvements achieved by ASAP versus well-known batch means procedures, especially in confidence-interval coverage probability.