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
Optimal mean-squared-error batch sizes
Management Science
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)
A spectral method for confidence interval generation and run length control in simulations
Communications of the ACM - Special issue on simulation modeling and statistical computing
An Introduction to the Regenerative Method for Simulation Analysis
An Introduction to the Regenerative Method for Simulation 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
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
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
Folded standardized time series area variance estimators for simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Overlapping Variance Estimators for Simulation
Operations Research
Efficient Computation of Overlapping Variance Estimators for Simulation
INFORMS Journal on Computing
Thirty years of "batch size effects"
Proceedings of the Winter Simulation Conference
Reflected variance estimators for simulation
Proceedings of the Winter Simulation Conference
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We extend and analyze a new class of estimators for the variance parameter of a steady-state simulation output process. These estimators are based on “folded” versions of the standardized time series (STS) of the process, and are analogous to the area and Cramér--von Mises estimators calculated from the original STS. In fact, one can apply the folding mechanism more than once to produce an entire class of estimators, all of which reuse the same underlying data stream. We show that these folded estimators share many of the same properties as their nonfolded counterparts, with the added bonus that they are often nearly independent of the nonfolded versions. In particular, we derive the asymptotic distributional properties of the various estimators as the run length increases, as well as their bias, variance, and mean squared error. We also study linear combinations of these estimators, and we show that such combinations yield estimators with lower variance than their constituents. Finally, we consider the consequences of batching, and we see that the batched versions of the new estimators compare favorably to benchmark estimators such as the nonoverlapping batch means estimator.