Kendall's advanced theory of statistics
Kendall's advanced theory of statistics
Management Science
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
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
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
Output analysis: ASAP2: an improved batch means procedure for simulation output analysis
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
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
On the robustness of batching estimators
Operations Research Letters
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We summarize the results of an extensive experimental performance evaluation of selected batch means procedures for building a confidence interval for a steady-state expected simulation response. We compare the performance of the well-known ABATCH and LBATCH procedures versus ASAP, a recently proposed variant of the method of nonoverlapping batch means (NOBM) that 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 correction is based on an inverted Cornish-Fisher expansion for the classical NOBM t-ratio, where the terms of the expansion are estimated via an autoregressive-moving average time series model of the batch means. Applying ABATCH, ASAP, and LBATCH to the analysis of a suite of twenty test problems involving discrete-time Markov chains, time-series processes, and queueing systems, we found ASAP to deliver confidence intervals that not only satisfy a user-specified absolute or relative precision requirement but also frequently outperform the corresponding confidence intervals delivered by ABATCH and LBATCH with respect to coverage probability.