Combining Standardized Time Series Area and Cramér–von Mises Variance Estimators
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Authors
Goldsman, David
Kang, Keebom
Kim, Seong-Hee
Seila, Andrew F.
Tokol, Gamze
Subjects
simulation
stationary process
variance estimation
standardized time series
area estimator
Cramér–von Mises estimator
Durbin–Watson estimator
batch means estimator
stationary process
variance estimation
standardized time series
area estimator
Cramér–von Mises estimator
Durbin–Watson estimator
batch means estimator
Advisors
Date of Issue
2006
Date
Publisher
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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.
Type
Article
Description
Series/Report No
Department
Graduate School of Business & Public Policy (GSBPP)
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Sponsors
This work was partially supported by National Science Foundation Grant DMI-0400260.
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Citation
Naval Research Logistics, Vol. 54 (2007)
Distribution Statement
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.