Properties of Standardized Time Series Weighted Area Variance Estimators

David Goldsman, Marc Meketon and Lee Schruben
Additional contact information
David Goldsman: School of ISyE, Georgia Institute of Technology, Atlanta, Georgia 30332
Marc Meketon: AT&T Bell Laboratories, Holmdel, New Jersey 07733
Lee Schruben: School of OR&IE, Cornell University, Ithaca, New York 14853

Management Science, 1990, vol. 36, issue 5, 602-612

Abstract: We wish to estimate the variance of the sample mean from a continuous-time stationary stochastic process. This article expands on the results of a technical note (Goldsman and Schruben 1990) by using the theory of standardized time series to investigate weighted generalizations of Schruben's area variance estimator. We find a simple expression for the bias of the weighted area variance estimator, and we give weights which yield variance estimators with lower asymptotic bias than certain other popular estimators. We use the weighted area variance estimators to derive asymptotically valid confidence interval estimators (CIEs) for the mean of a stationary stochastic process. Although the weighted area CIEs have the same asymptotic expected value and variance of the length as Schruben's area CIE, we show that the new CIEs sometimes yield coverages which are closer to the nominal value.

Keywords: simulation output analysis; confidence intervals; standardized time series (search for similar items in EconPapers)
Date: 1990
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:36:y:1990:i:5:p:602-612

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