Properties of standardized time series weighted area variance estimators
Management Science
Simulation output analysis using standardized time series
Mathematics of Operations Research
Confidence intervals using orthonormally weighted standardized time series
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Cramer-Von Mises Variance Estimators for Simulations
Operations Research
Overlapping batch means: something for nothing?
WSC '84 Proceedings of the 16th conference on Winter simulation
Simulation Modeling and Analysis with Expertfit Software
Simulation Modeling and Analysis with Expertfit Software
Simulation output analysis using integrated paths
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Permuted Standardized Time Series for Steady-State Simulations
Mathematics of Operations Research
Performance of folded variance estimators for simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An improved standardized time series Durbin-Watson variance estimator for steady-state simulation
Operations Research Letters
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We study reflected standardized time series (STS) estimators for the asymptotic variance parameter of a stationary stochastic process. These estimators are based on the concept of data re-use and allow us to obtain more information about the process with no additional sampling effort. Reflected STS estimators are computed from "reflections" of the original sample path. We show that it is possible to construct linear combinations of reflected estimators with smaller variance than the variance of each constituent estimator, often at no cost in bias. We provide Monte Carlo examples to show that the estimators perform as well in practice as advertised by the theory.