An analysis is given for an extensive experimental performance evaluation of Skart, an automated sequential batch means procedure for constructing an asymptotically valid confidence interval (CI) on the steady-state mean of a simulation output process. Skart is designed to deliver a CI satisfying user-specified requirements on absolute or relative precision as well as coverage probability. Skart exploits separate adjustments to the half-length of the classical batch means 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. 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 steady-state simulation analysis methods---namely, its predecessors ASAP3, WASSP, and SBatch, as well as ABATCH, LBATCH, the Heidelberger--Welch procedure, and the Law--Carson procedure. Specifically, Skart exhibited competitive sampling efficiency and closer conformance to the given CI coverage probabilities than the other procedures, especially in the most difficult test processes.
['Ali Tafazzoli', 'James R. Wilson', 'Emily K. Lada', 'Natalie M. Steiger']
Performance of Skart: A Skewness-and Autoregression-Adjusted Batch Means Procedure for Simulation Analysis