Combining standardized time series area and Cramér–von Mises variance estimators

Combining standardized time series area and Cramér–von Mises variance estimators

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Article ID: iaor20082042
Country: United States
Volume: 54
Issue: 4
Start Page Number: 384
End Page Number: 396
Publication Date: Jun 2007
Journal: Naval Research Logistics
Authors: , , , ,
Keywords: statistics: general
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.

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