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dc.contributor.authorGoldsman, David
dc.contributor.authorKang, Keebom
dc.contributor.authorKim, Seong-Hee
dc.contributor.authorSeila, Andrew F.
dc.contributor.authorTokol, Gamze
dc.date.accessioned2015-08-19T21:39:32Z
dc.date.available2015-08-19T21:39:32Z
dc.date.issued2006
dc.identifier.citationNaval Research Logistics, Vol. 54 (2007)en_US
dc.identifier.urihttp://hdl.handle.net/10945/46102
dc.description.abstractWe 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.en_US
dc.description.sponsorshipThis work was partially supported by National Science Foundation Grant DMI-0400260.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleCombining Standardized Time Series Area and Cramér–von Mises Variance Estimatorsen_US
dc.typeArticleen_US
dc.contributor.departmentGraduate School of Business & Public Policy (GSBPP)en_US
dc.subject.authorsimulationen_US
dc.subject.authorstationary processen_US
dc.subject.authorvariance estimationen_US
dc.subject.authorstandardized time seriesen_US
dc.subject.authorarea estimatoren_US
dc.subject.authorCramér–von Mises estimatoren_US
dc.subject.authorDurbin–Watson estimatoren_US
dc.subject.authorbatch means estimatoren_US


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