Experimental performance evaluation of batch means procedures for simulation output analysis
Proceedings of the 32nd conference on Winter simulation
Output analysis: output data analysis for simulations
Proceedings of the 33nd conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Statistical analysis of simulation output: output data analysis for simulations
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Output analysis: ASAP2: an improved batch means procedure for simulation output analysis
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Steady-state simulation analysis using ASAP3
WSC '04 Proceedings of the 36th conference on Winter simulation
Overlapping variance estimators for simulations
WSC '04 Proceedings of the 36th conference on Winter simulation
Linear combinations of overlapping variance estimators for simulations
WSC '05 Proceedings of the 37th conference on Winter simulation
Performance evaluation of ASAP3 for steady-state output analysis
WSC '05 Proceedings of the 37th conference on Winter simulation
A distribution-free tabular CUSUM chart for correlated data with automated variance estimation
Proceedings of the 40th Conference on Winter Simulation
Cutset sampling for Bayesian networks
Journal of Artificial Intelligence Research
Performance of folded variance estimators for simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
INFORMS Journal on Computing
INFORMS Journal on Computing
A new perspective on batched quantile estimation
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
| Hi-index | 0.00 |
We examine key convergence properties of the steady-state simulation analysis method of nonoverlapping batch means (NOBM) when it is applied to a stationary, phi-mixing process. For an increasing batch size and a fixed batch count, we show that the standardized vector of batch means converges in distribution to a vector of independent standard normal variates--a well-known result underlying the NOBM method for which there appears to be no direct, readily accessible justification. To characterize the asymptotic behavior of the classical NOBM confidence interval for the mean response, we formulate certain moment conditions on the components (numerator and denominator) of the associated Student'st-ratio that are necessary to ensure the validity of the confidence interval. For six selected stochastic systems, we summarize an extensive numerical analysis of the convergence to steady-state limits of these moment conditions; and for two systems we present a simulation-based analysis exemplifying the corresponding convergence in distribution of the components of the NOBMt-ratio. These results suggest that in many simulation output processes, approximate joint normality of the batch means is achieved at a substantially smaller batch size than is required to achieve approximate independence; and an improved batch means method should exploit this property whenever possible.