Skart: A skewness- and autoregression-adjusted batch-means procedure for simulation analysis

We discuss Skart, an automated batch-means procedure for constructing a skewness- and autoregression-adjusted confidence interval for the steady-state mean of a simulation output process. Skart is a sequential procedure designed to deliver a confidence interval that satisfies user-specified requirements concerning not only coverage probability but also the absolute or relative precision provided by the half-length. Skart exploits separate adjustments to the half-length of the classical batch-means confidence interval so as to account for the effects on the distribution of the underlying Student's t-statistic that arise from nonnormality and autocorrelation of the batch means. Skart also delivers a point estimator for the steady-state mean that is approximately free of initialization bias. In an experimental performance evaluation involving a wide range of test processes, Skart compared favorably with other simulation analysis methods--namely, its predecessors ASAP3, WASSP, and SB...

• Type: Conference
• Year: 2008
• Where: WSC
• Authors: Ali Tafazzoli, James R. Wilson, Emily K. Lada, Natalie M. Steiger
• Keywords: Autoregression-adjusted Confidence Interval, Confidence Interval, Modeling And Simulation, Steady-state Mean, WSC 2008