New confidence interval estimators using standardized time series
Management Science
Using permutations in regenerative simulations to reduce variance
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on modeling and analysis of stochastic systems
Standardized Time Series LP-Norm Variance Estimators for Simulations
Management Science
Improving standardized time series methods by permuting path segments
Proceedings of the 33nd conference on Winter simulation
Central Limit Theorems for Permuted Regenerative Estimators
Operations Research
Permuted weighted area estimators
WSC '04 Proceedings of the 36th 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
Proceedings of the 40th Conference on Winter Simulation
Performance of folded variance estimators for simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Reflected variance estimators for simulation
Proceedings of the Winter Simulation Conference
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We describe an extension procedure for constructing new standardized time series procedures from existing ones. The approach is based on averaging over sample paths obtained by permuting path segments. Analytical and empirical results indicate that permuting improves standardized time series methods. We compare permuting to an alternative extension procedure known as batching. We demonstrate the permuting method by applying it to estimators based on the maximum and the area of a normalized path.