N-SKART: A NONSEQUENTIAL SKEWNESS- AND AUTOREGRESSION-ADJUSTED BATCH-MEANS PROCEDURE FOR SIMULATION ANALYSIS

We discuss N-Skart, a nonsequential procedure designed to deliver a confidence interval (Cl) for the steady-state mean of a simulation output process when the user supplies a single simulation-generated time series of arbitrary size and specifies the required coverage probability for a CI based on that data set. N-Skart is a variant of the method of batch means that exploits separate adjustments to the half-length of the CI so as to account for the effects on the distribution of the underlying Student's t-statistic that arise from skewness (nonnormality) and autocorrelation of the batch means. If the sample size is sufficiently large, then N-Skart delivers not only a CI but also 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 and sample sizes, N-Skart exhibited close conformance to the user-specified CI coverage probabilities.