Cramer-von Mises Variance Estimators for Simulations
Seila, Andrew F.
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We study estimators for the variance parameter u 2 of a stationary process. The estimators are based on weighted Cramer-van Mises statistics formed from the standardized time series of the process. Certain weightings yield estimators which are "first-order unbiased" for u2 and which have low variance. We also show how the Cramer-von Mises estimators are related to the standardized time series area estimator; we use this relationship to establish additional estimators for u2 .
Proceedings of the 1991 Winter Simulation Conference Barry L. Nelson, W. David Kelton, Gordon M. Clark (eds.)
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Goldsman, David; Kang, Keebom; Kim, Seong-Hee; Seila, Andrew F.; Tokol, Gamze (2006);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 ...
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Seila, Andrew F.; Goldsman, David; Kang, Keebom (Monterey, California. Naval Postgraduate School, 1993-09); NPS-AS-93-028We study estimators for the variance parameter sigma(2) of a stationary process. The estimators are based on weightings yield estimators that are 'first-order unbiased' for sigma (2) We derive an expression for the asymptotic ...