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

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

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

Full text

Pdf (406 KB)

Source

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

Bibliometrics

Downloads (6 Weeks): 7, Downloads (12 Months): 28, Citation Count: 2

Additional Information:

abstract references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1029174.1029175 What is a DOI?

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.

	1	Charles R. Cash , Barry L. Nelson , David G. Dippold , J. Mark Long , William P. Pollard, Evaluation of tests for initial-condition bias, Proceedings of the 24th conference on Winter simulation, p.577-585, December 13-16, 1992, Arlington, Virginia, United States [doi>10.1145/167293.167640]
	2	Glynn, P. W. 1984. Some asymptotic formulas for markov chain with applications to simulation. J. Stat. Comput. Simul. 19, 97--112.
	3	Glynn, P. W. 1989. A GSMP formalism for discrete event systems. Proc. IEEE 77. 14--23.
	4	Glynn, P. W. 1994. Some topics in regenerative steady-state simulation. Acta Appl. Math. 34, 225--236.
	5	Peter W. Glynn, Some new results on the initial transient problem, Proceedings of the 27th conference on Winter simulation, p.165-170, December 03-06, 1995, Arlington, Virginia, United States [doi>10.1145/224401.224458]
	6	Peter W. Glynn , Philip Heidelberger, Bias properties of budget constrained simulations, Operations Research, v.38 n.5, p.801-814, Sep./Oct. 1990 [doi>10.1287/opre.38.5.801]
	7	Glynn, P. W. and Heidelberger, P. 1991a. Analysis of initial transient deletion for replicated steady-state simulations. Oper. Res. Lett. 10, 437--443.
	8	Peter W. Glynn , Philip Heidelberger, Analysis of parallel replicated simulations under a completion time constraint, ACM Transactions on Modeling and Computer Simulation (TOMACS), v.1 n.1, p.3-23, Jan. 1991 [doi>10.1145/102810.102811]
	9	Peter W. Glynn , Philip Heidelberger, Analysis if initial transient deletion for parallel steady-state simulations, SIAM Journal on Scientific and Statistical Computing, v.13 n.4, p.904-922, July 1992 [doi>10.1137/0913054]
	10	Glynn, P. W. and Heidelberger, P. 1992b. Jackknifing under a budget constraint. ORSA J. Comput. 4, 226--234.
	11	Goldsman, D., Schruben, L., and Swain., J. 1994. Tests for transient means in simulated time series. Naval Res. Logist. Quart. 41, 171--187.
	12	Shane G. Henderson , Peter W. Glynn, Regenerative steady-state simulation of discrete-event systems, ACM Transactions on Modeling and Computer Simulation (TOMACS), v.11 n.4, p.313-345, October 2001 [doi>10.1145/508366.508367]
	13	Iglehart, D. L. 1975. Simulating stable stochastic systems, V: Comparison of ratio estimators. Naval Res. Logist. Quart. 22, 553--565.
	14	Meketon, M. and Heidelberger, P. 1982. A renewal theoretic approach to bias reduction in regenerative simulations. Manage. Sci. 26, 173--181.
	15	Nelson, B. 1992. Initial-condition bias. In Handbook of Industrial Engineering, 2 ed., G. Salvendy, Ed. Wiley, New York.
	16	Schruben, L. 1982. Detecting initialization bias in simulation output. Oper. Res. 30, 3, 151--153.
	17	Schruben, L., Singh, H., and Tierney, L. 1983. Optimal tests for initialization bias in simulation output. Oper. Res. 31, 6, 1167--1178.
	18	K. Preston White, Jr., An effective truncation heuristic for bias reduction in simulation output, Simulation, v.69 n.6, p.323-334, Dec. 1997 [doi>10.1177/003754979706900601]
	19	K. Preston White, Jr. , Michael J. Cobb , Stephen C. Spratt, A comparison of five steady-state truncation heuristics for simulation, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida

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