A comprehensive review of methods for simulation output analysis
Proceedings of the 38th conference on Winter simulation
Fortieth anniversary special panel: Landmark papers
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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
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
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
Ranking and selection techniques with overlapping variance estimators
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A distribution-free tabular CUSUM chart for correlated data with automated variance estimation
Proceedings of the 40th Conference on Winter Simulation
Simulation output analysis using integrated paths II: Low bias estimators
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Performance of folded variance estimators for simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Linear combinations of overlapping variance estimators for simulation
Operations Research Letters
An improved standardized time series Durbin-Watson variance estimator for steady-state simulation
Operations Research Letters
A new perspective on batched quantile estimation
Proceedings of the Winter Simulation Conference
Thirty years of "batch size effects"
Proceedings of the Winter Simulation Conference
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
Overlapping batches for the assessment of solution quality in stochastic programs
Proceedings of the Winter Simulation Conference
| Hi-index | 0.00 |
To estimate the variance parameter (i.e., the sum of covariances at all lags) for a steady-state simulation output process, we formulate certain statistics that are computed from overlapping batches separately and then averaged over all such batches. We form overlapping versions of the area and Cramér--von Mises estimators using the method of standardized time series. For these estimators, we establish (i) their limiting distributions as the sample size increases while the ratio of the sample size to the batch size remains fixed; and (ii) their mean-square convergence to the variance parameter as both the batch size and the ratio of the sample size to the batch size increase. Compared with their counterparts computed from nonoverlapping batches, the estimators computed from overlapping batches asymptotically achieve reduced variance while maintaining the same bias as the sample size increases; moreover, the new variance estimators usually achieve similar improvements compared with the conventional variance estimators based on nonoverlapping or overlapping batch means. In follow-up work, we present several analytical and Monte Carlo examples, and we formulate efficient procedures for computing the overlapping estimators with only order-of-sample-size effort.