Inverse-transformation algorithms for some common stochastic processes
WSC '89 Proceedings of the 21st conference on Winter simulation
Variance of the sample mean: properties and graphs of quadratic-form estimators
Operations Research
Optimal mean-squared-error batch sizes
Management Science
Computational efficiency of batching methods
Proceedings of the 29th conference on Winter simulation
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Simulation output analysis via dynamic batch means
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Overlapping batch means: something for nothing?
WSC '84 Proceedings of the 16th conference on Winter simulation
Simulation output analysis via dynamic batch means
Proceedings of the 32nd conference on Winter simulation
Simulation output analysis: a tutorial based on one research thread
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation output analysis using integrated paths
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 40th Conference on Winter Simulation
Using excursions to analyze simulation output
Probability in the Engineering and Informational Sciences
| Hi-index | 0.00 |
This paper is focused on estimating the quality of the sample mean from a steady-state simulation experiment with consideration of computational efficiency, memory requirement, and statistical efficiency. In addition, we seek methods that do not require knowing run length a priori. We develop an algorithm of nonoverlapping batch means that is implemented in fixed memory by dynamically changing both batch size and number of batches as the simulation runs. The algorithm, denoted by DBM for Dynamic Batch Means, requires computation time similar to other batch means data-collection methods, despite its fixed memory requirement. To achieve satisfactory statistical efficiency of DBM, we propose two associated estimators, VT B M and VP B M, of the variance of the sample mean and investigate their statistical properties. Our study shows that the estimator VP B M with parameter w = 1 is, as a practical matter, better than the other proposed estimators.