Combining standardized time series area and Cramér–von Mises variance estimators |
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| Authors: | David Goldsman Keebom Kang Seong‐Hee Kim Andrew F. Seila Gamze Tokol |
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| Affiliation: | 1. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GeorgiaH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia;2. Graduate School of Business and Public Policy, Naval Postgraduate School, Monterey, California;3. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia;4. Terry College of Business, University of Georgia, Athens, Georgia;5. Decision Analytics, Atlanta, Georgia |
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| Abstract: | We propose three related estimators for the variance parameter arising from a steady‐state simulation process. All are based on combinations of standardized‐time‐series area and Cramér–von Mises (CvM) estimators. The first is a straightforward linear combination of the area and CvM estimators; the second resembles a Durbin–Watson statistic; and the third is related to a jackknifed version of the first. The main derivations yield analytical expressions for the bias and variance of the new estimators. These results show that the new estimators often perform better than the pure area, pure CvM, and benchmark nonoverlapping and overlapping batch means estimators, especially in terms of variance and mean squared error. We also give exact and Monte Carlo examples illustrating our findings.© 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007 |
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| Keywords: | simulation stationary process variance estimation standardized time series area estimator Cramé r– von Mises estimator Durbin– Watson estimator batch means estimator |
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