New confidence interval estimators using standardized time series
Management Science
Properties of standardized time series weighted area variance estimators
Management Science
Simulation output analysis using standardized time series
Mathematics of Operations Research
Strong consistency and other properties of the spectral variance estimator
Management Science
Variance of the sample mean: properties and graphs of quadratic-form estimators
Operations Research
Strong consistency of the variance estimator in steady-state simulation output analysis
Mathematics of Operations Research
Optimal mean-squared-error batch sizes
Management Science
Asymptotic and finite-sample correlations between OBM estimators
WSC '93 Proceedings of the 25th conference on Winter simulation
Large-sample results for batch means
Management Science
On the relationship between batch means, overlapping means and spectral estimation
WSC '87 Proceedings of the 19th conference on Winter simulation
Confidence intervals using orthonormally weighted standardized time series
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Cramer-Von Mises Variance Estimators for Simulations
Operations Research
Convergence Properties of the Batch Means Method for Simulation Output Analysis
INFORMS Journal on Computing
Overlapping batch means: something for nothing?
WSC '84 Proceedings of the 16th conference on Winter simulation
Confidence interval estimation using linear combinations of overlapping variance estimators
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Linear combinations of overlapping variance estimators for simulation
Operations Research Letters
Thirty years of "batch size effects"
Proceedings of the Winter Simulation Conference
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We examine properties of linear combinations of overlapping standardized time series area estimators for the variance parameter of a stationary stochastic process. We find that the linear combination estimators have lower bias and variance than their overlapping constituents and nonoverlapping counterparts; in fact, the new estimators also perform particularly well against the benchmark batch means estimator. We illustrate our findings with analytical and Monte Carlo examples.