Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
Batching methods in simulation output analysis: what we know and what we don't
WSC '96 Proceedings of the 28th conference on Winter simulation
Asymptotic and finite-sample correlations between OBM estimators
WSC '93 Proceedings of the 25th conference on Winter simulation
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Simulation output analysis: a tutorial based on one research thread
WSC '04 Proceedings of the 36th conference on Winter simulation
Proceedings of the 40th Conference on Winter Simulation
Thirty years of "batch size effects"
Proceedings of the Winter Simulation Conference
| Hi-index | 0.00 |
We continue our investigation of linear combinations of variance-of-the-sample-mean estimators that are parameterized by batch size. First we state the mse-optimal linear-combination weights in terms of the bias vector and the covariance matrix of the component estimators for two cases: weights unconstrained and weights constrained to sum to one. Then we report a small numerical study that demonstrates mse reduction of about 80% for unconstrained weights and about 30% for constrained weights. The mse's and the percent reductions are similar for all four estimator types considered. Such large mse reductions could not be achieved in practice, since they assume knowledge of unknown parameters, which would have to be estimated. Optimal-weight estimation is not considered here.