Parametric inference for generalized semi-Markov processes

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Parametric inference for generalized semi-Markov processes

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Winter Simulation Conference archive
Proceedings of the 25th conference on Winter simulation table of contents

Los Angeles, California, United States

Pages: 323 - 328

Year of Publication: 1993

ISBN:0-7803-1381-X

Author

Halem Damerdji

Department of Industrial Engineering, North Carolina State University, Raleigh, NC

Sponsors

IEEE-CS : Computer Society
IEEE-SMCS : Systems, Man & Cybernetics Society
ACM: Association for Computing Machinery
ORSA : Operations Research Society of America
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
SCS : Society for Computer Simulation
ASA : American Statistical Association
NIST : National Institue of Standards & Technology
TIMS/CSG :

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 4, Downloads (12 Months): 13, Citation Count: 1

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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	Barlow, R. E. and Proschan, F. 1975. Statistical Theory of Reliability and Life Testing: Probability Models. Holt, Rinehart and Winston, New York.
	2	Basawa, I. V. and Prakasa Rao, B. L. S. 1980. Statistical Inference for Stochastic Processes. Academic Press, London.
	3	Billingsley, P. 1961a. Statistical methods in Markov chains. Annals of Mathematical Statistics, 32, 12- 40.
	4	Billingsley, P. 1961b. Statistical Inference for Markov Processes. Chicago" University of Chicago Press.
	5	Bilhngsley, P. 1986. Probability and Measure, 2nd edn. Wiley, New York.
	6	Cox, D. R. and Hinkley, D. V. 1974. Theoretical Statistics. Chapman and Hall, London.
	7	Damerdji, H. 1992. Maximum likelihood estimation for generahzed semi-Markov processes. Technical Report #92-11, Department of Industrial Engineering, North Carolina State University, Raleigh, NC.
	8	Glynn, P. W. 1988. A GSMP formalism for discreteevent systems. Proceedings of the IEEE, 77, 14-23.
	9	P W. Glynn , D. L. Iglehart, Importance sampling for stochastic simulations, Management Science, v.35 n.11, p.1367-1392, Nov. 1989 [doi>10.1287/mnsc.35.11.1367]
	10	K~nig, D., Matthes, K., and Nawrotzki, W. K. 1967. Verallgemeinerungen der Erlangschen und Engsetschen Formeln. Akademie-Verlag. Berfin.
	11	Moore, E. tI. and Pyke, R. 1968. Estimation of the transition distributions of a Markov renewal process. Ann. Inst. Star. Math., 20, 411-424.
	12	Whirr, W. 1980. Continuity of generalized semi- Markov processes. Mathematics of Operations Research, 5, 494-501.

CITED BY

Mukesh Dalal , Brett Groel , Armand Prieditis, Dynamic scheduling I: real-time decision making using simulation, Proceedings of the 35th conference on Winter simulation: driving innovation, December 07-10, 2003, New Orleans, Louisiana

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