Combining Standardized Time Series Area and Cramér–von Mises Variance Estimators
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Author
Goldsman, David
Kang, Keebom
Kim, Seong-Hee
Seila, Andrew F.
Tokol, Gamze
Date
2006Metadata
Show full item recordAbstract
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.
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