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Combining standardized time series area and Cramér–von Mises variance estimators
Author(s) -
Goldsman David,
Kang Keebom,
Kim SeongHee,
Seila Andrew F.,
Tokol Gamze
Publication year - 2007
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.20214
Subject(s) - estimator , statistic , mathematics , variance (accounting) , series (stratigraphy) , benchmark (surveying) , statistics , m estimator , monte carlo method , paleontology , accounting , biology , geodesy , business , geography
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