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 SBatch as well as ABATCH, LBATCH, the Heidelberger-Welch procedure, and the Law-Carson procedure.