Paper Info
Title
N-Skart: A Nonsequential Skewness- and Autoregression-adjusted Batch-means Procedure for Simulation Analysis
Abstract
We discuss N-Skart, a nonsequential procedure designed to deliver a confidence interval (CI) 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.
Year
DOI
Venue
2009
10.1109/WSC.2009.5429565
Winter Simulation Conference
Keywords
Field
DocType
Analytical models,Steady-state,Computational modeling,Computational Intelligence Society,Time series analysis,Autocorrelation,Testing,Probability,Statistical analysis,Robustness
Point estimation,Autoregressive model,Skewness,Initialization,Confidence interval,Statistics,Coverage probability,Sample size determination,Mathematics,Autocorrelation
Conference
Volume
Issue
ISSN
56
2
0891-7736
ISBN
Citations 
PageRank 
978-1-4244-5771-7
4
0.53
References 
Authors
12
2
Name
Order
Citations
PageRank
Ali Gerhard Tafazzoli1414.06
James R. Wilson2840143.42