Empirical performance of bias-reducing estimators for regenerative steady-state simulations

Authors:
Ming-Hua Hsieh;Donald L. Iglehart;Peter W. Glynn
Affiliations:
National Chengchi University, Taiwan;Stanford University, Stanford, CA;Stanford University, Stanford, CA
Venue:
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Year:
2004

Citing 8
Cited 5

Bias properties of budget constrained simulations

Operations Research
Analysis of parallel replicated simulations under a completion time constraint

ACM Transactions on Modeling and Computer Simulation (TOMACS)
Analysis if initial transient deletion for parallel steady-state simulations

SIAM Journal on Scientific and Statistical Computing
Evaluation of tests for initial-condition bias

WSC '92 Proceedings of the 24th conference on Winter simulation
Some new results on the initial transient problem

WSC '95 Proceedings of the 27th conference on Winter simulation
An effective truncation heuristic for bias reduction in simulation output

Simulation
Regenerative steady-state simulation of discrete-event systems

ACM Transactions on Modeling and Computer Simulation (TOMACS)
A comparison of five steady-state truncation heuristics for simulation

Proceedings of the 32nd conference on Winter simulation

On the theoretical comparison of low-bias steady-state estimators

ACM Transactions on Modeling and Computer Simulation (TOMACS)
A new approach for parallel steady-state simulations

Proceedings of the 38th conference on Winter simulation
New estimators for parallel steady-state simulations

Winter Simulation Conference
The initial transient in steady-state point estimation: contexts, a bibliography, the mse criterion, and the mser statistic

Proceedings of the Winter Simulation Conference
A regenerative bootstrap approach to estimating the initial transient

Proceedings of the Winter Simulation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

When simulating a stochastic system, simulationists often are interested in estimating various steady-state performance measures. The classical point estimator for such a measure involves simply taking the time average of an appropriate function of the process being simulated. Since the simulation can not be initiated with the (unknown) steady-state distribution, the classical point estimator is generally biased. In the context of regenerative steady-state simulation, a variety of other point estimators have been developed in an attempt to minimize the bias. In this paper, we provide an empirical comparison of these estimators in the context of four different continuous-time Markov chain models. The bias of the point estimators and the coverage probabilities of the associated confidence intervals are reported for the four models. Conclusions are drawn from this experimental work as to which methods are most effective in reducing bias.