Covariance of regenerative mean and variance estimators for Markov chains

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Covariance of regenerative mean and variance estimators for Markov chains

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Winter Simulation Conference archive
Proceedings of the 20th conference on Winter simulation table of contents

San Diego, California, United States

Pages: 473 - 475

Year of Publication: 1988

ISBN:0-911801-42-1

Author

James M. Calvin

Department of Operations Research, Stanford University, Stanford, California

Sponsors

ORS : Orthopaedic Research Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling
TIMS :
IEEE-CS : Computer Society
IEEE-SMCS : Systems, Man & Cybernetics Society

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 6, Downloads (12 Months): 16, Citation Count: 1

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ABSTRACT

This paper explores the asymptotic covariance structure of the mean and standard deviation estimators used in the regenerative method of simulation output analysis. It is shown that the asymptotic covariance of the mean and standard deviation estimators does not depend on the choice of return state.

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	Chung, K.L. (1967). Markov Chains with Stationary Transition Probabilities, Second Edition. Springer-Verlag, Berlin-New York-tteidelberg.
	2	Glynn, P.W. and D.L. Iglehart. (1987). A Joint Central Limi~ Theorem for the Sample Mean and Regenerative Variance Estimator. A~mals of Operations Research 8, 41-55.
	3	Orey, S. (1971). Lecture Notes on the Limit Theorems for Markov Chain Transition Probabilities. Van Nostrand, New York.