A decision-theoretic approach to screening and selection with common random numbers

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A decision-theoretic approach to screening and selection with common random numbers

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Winter Simulation Conference archive
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1 table of contents

Phoenix, Arizona, United States

Pages: 603 - 610

Year of Publication: 1999

ISBN:0-7803-5780-9

Authors

Stephen E. Chick	Department of Industrial and Operations Engineering, The University of Michigan, 1205 Beal Avenue, Ann Arbor, MI
Koichiro Inoue	Department of Industrial and Operations Engineering, The University of Michigan, 1205 Beal Avenue, Ann Arbor, MI

Sponsors

ACM: Association for Computing Machinery
SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 2, Downloads (12 Months): 12, Citation Count: 3

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references cited by index terms collaborative colleagues

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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	Anderson, T. W. 1957. Maximum likelihood estimates for a multivariate normal distribution when some observa-tions are missing. Journal of the American Statistical Association 52, 200-203.
	2	Banks, J., J. S. Carson, and B. L. Nelson. 1996. Discrete-Event System Simulation (2nd ed.). Upper Saddle River, NJ, USA: Prentice-Hall, Inc.
	3	Bechhofer, R. E., T. J. Santner, and D. M. Goldsman. 1995. Design and Analysis for Statistical Selection, Screening, and Multiple Comparisons. New York: John Wiley & Sons, Inc.
	4	Berger, J. O. 1988. A Bayesian approach to ranking and selection of related means with alternatives to analysis-ofvariance methodology. Journal of the American Statistical Association 83(402), 364-373.
	5	Bernardo, J. M. and A. F. M. Smith. 1994. Bayesian Theory. Chichester, UK: Wiley.
	6	Stephen E. Chick , Koichiro Inoue, New Procedures to Select the Best Simulated System Using Common Random Numbers, Management Science, v.47 n.8, p.1133-1149, August 2001 [doi>10.1287/mnsc.47.8.1133.10229]
	7	Chick, S. E. and K. Inoue. 1999b. New two-stage and sequential procedures for selecting the best simulated system. in submission.
	8	Gordon M. Clark , Wei-ning Yang, A Bonferroni selection procedure when using commom random numbers with unknown variances, Proceedings of the 18th conference on Winter simulation, p.313-315, December 08-10, 1986, Washington, D.C., United States [doi>10.1145/318242.318452]
	9	de Groot, M. H. 1970. Optimal Statistical Decisions.New York: McGraw-Hill, Inc.
	10	David Goldsman , Barry L. Nelson, Statistical screening, selection, and multiple comparison procedures in computer simulation, Proceedings of the 30th conference on Winter simulation, p.159-166, December 13-16, 1998, Washington, D.C., United States
	11	Y. Hochberg , A. C. Tamhane, Multiple comparison procedures, John Wiley & Sons, Inc., New York, NY, 1987
	12	Koichiro Inoue , Stephen E. Chick, Comparison of Bayesian and frequentist assessments of uncertainty for selecting the best system, Proceedings of the 30th conference on Winter simulation, p.727-734, December 13-16, 1998, Washington, D.C., United States
	13	Koenig, L. W. and A. M. Law. 1985. A procedure for selecting a subset of size m containing the ` best of k independent normal populations, with applications to simulation. Commun. Statist.-Simulation and Compu-tation 14(3), 719-734.
	14	Averill M. Law , W. David Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1997
	15	Barry L. Nelson , Frank J. Matejcik, Using common random numbers for indifference-zone selection and multiple comparisons in simulation, Management Science, v.41 n.12, p.1935-1945, Dec. 1995 [doi>10.1287/mnsc.41.12.1935]
	16	Nelson, B. L., J. Swann, D. Goldsman, and W. Song. 1998. Simple procedures for selecting the best simu-lated system when the number of alternatives is large. Northwestern University, IEMS Technical Report.

CITED BY 3

	Sigurdur Ólafsson , Jumi Kim, Simulation optimization: simulation optimization, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
	Jumi Kim , Sigurdur Ólafsson, Optimization via simulation: two-stage NP method with inheritance, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
	Stephen E. Chick , Koichiro Inoue, New results on procedures that select the best system using CRN, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida