Cramer-Von Mises Variance Estimators for Simulations
Seila, Andrew F.
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We 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 variance of the new estimators; this expression is then used to obtain the first-order unbiased estimator having the smallest variance among fixed-degree polynomial weighting functions. Although our work is based on asymptotic theory, we present exact and empirical examples to demonstrate the new estimators' small-sample robustness.
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NPS Report NumberNPS-AS-93-028
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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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