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. 978-1-4244-2708-6/08/$25.00
THE METHOD OF BATCH MEANSIn the method of nonoverlapping batch means (NBM), the outputs fX i W i D 1; : : : ng are divided into k adjacent nonoverlapping batches, each of size m, where we assume n D km and both k and m are sufficiently large to ensure that the resulting batch means are at least approximately independent and identically distributed (i.i.d.) normal random variables. The sample mean for the j th batch is AUTHOR BIOGRAPHIES