Computational efficiency of batching methods
Proceedings of the 29th conference on Winter simulation
The impact of transients on simulation variance estimators
Proceedings of the 29th conference on Winter simulation
Selection procedures with standardized time series variance estimators
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Permuted weighted area estimators
WSC '04 Proceedings of the 36th conference on Winter simulation
Overlapping variance estimators for simulations
WSC '04 Proceedings of the 36th conference on Winter simulation
Review of advanced methods for simulation output analysis
WSC '05 Proceedings of the 37th conference on Winter simulation
Linear combinations of overlapping variance estimators for simulations
WSC '05 Proceedings of the 37th conference on Winter simulation
A comprehensive review of methods for simulation output analysis
Proceedings of the 38th conference on Winter simulation
Proceedings of the 38th conference on Winter simulation
Statistical analysis of simulation output: state of the art
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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
On the robustness of batching estimators
Operations Research Letters
Overlapping batch means: something more for nothing?
Proceedings of the Winter Simulation Conference
On the mean-squared error of variance estimators for computer simulations
Proceedings of the Winter Simulation Conference
Reflected variance estimators for simulation
Proceedings of the Winter Simulation Conference
Variance estimation and sequential stopping in steady-state simulations using linear regression
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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We study estimators for the variance parameter σ2 of a stationary process. The estimators are based on weighted Cramer-von Mises statistics, and certain weightings yield estimators that are "first-order unbiased" for σ2. 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. Our work is based on asymptotic theory; however, we present exact and empirical examples to demonstrate the new estimators' small-sample robustness. We use a single batch of observations to derive the estimators' asymptotic properties, and then we compare the new estimators among one another. In real-life applications, one would use more than one batch; we indicate how this generalization can be carried out.