Cramer-von Mises variance estimators for simulations
By: by David Goldsman, Keebom Kang, Andrew F. Seila
Contributors: Kang, Keebom; Seila, Andrew F.; Naval Postgraduate School (U.S.). Dept. of Administrative Sciences
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1993
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 23
Subjects: WEIGHTING FUNCTIONS; ORDER  STATISTICS; ESTIMATES; ANALYSIS OF VARIANCE
Other titles: NPS-AS-93-028.
Description: 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. Simulation, Stationary process, Variance estimation,      Standardized time series, Cramer-von mises estimator.
Notes: Title from cover.

"NPS-AS-93-028."

"September 1993."

AD A278 799.

Includes bibliographical references (p. 22-23)

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