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

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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...
Ali Tafazzoli, James R. Wilson, Emily K. Lada, Nat
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where WSC
Authors Ali Tafazzoli, James R. Wilson, Emily K. Lada, Natalie M. Steiger
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