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

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

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ACM Transactions on Modeling and Computer Simulation (TOMACS) archive
Volume 14 , Issue 4 (October 2004) table of contents

Pages: 325 - 343

Year of Publication: 2004

ISSN:1049-3301

Authors

Ming-Hua Hsieh	National Chengchi University, Taiwan
Donald L. Iglehart	Stanford University, Stanford, CA
Peter W. Glynn	Stanford University, Stanford, CA

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 2, Downloads (12 Months): 22, Citation Count: 2

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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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 2

	Ming-hua Hsieh, A new approach for parallel steady-state simulations, Proceedings of the 37th conference on Winter simulation, December 03-06, 2006, Monterey, California
	Hernan P. Awad , Peter W. Glynn, On the theoretical comparison of low-bias steady-state estimators, ACM Transactions on Modeling and Computer Simulation (TOMACS), v.17 n.1, p.4-es, January 2007