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Two-stage procedures for multiple comparisons with a control in steady-state simulations
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Source Winter Simulation Conference archive
Proceedings of the 28th conference on Winter simulation table of contents
Coronado, California, United States
Pages: 372 - 375  
Year of Publication: 1996
ISBN:0-7803-3383-7
Authors
Halim Damerdji  Department of Industrial Engineering, North Carolina State University, Raleigh, NC
Marvin K. Nakayama  Department of Computer and Information Science, New Jersey Institute of Technology, Newark, NJ
Sponsors
INFORMS/CS : Computer Science TC
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
IEEE-CS : Computer Society
IEEE-SMCS : Systems, Man & Cybernetics Society
ACM: Association for Computing Machinery
Publisher
IEEE Computer Society  Washington, DC, USA
Bibliometrics
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ABSTRACT

Suppose that we have k different stochastic systems, where /spl mu/i denotes the steady-state mean of system i. We assume that the system labeled k is a control and want to compare the performance of the other sys tems, labeled 1,2,...,k - 1, relative to this control. This problem is known in the statistical literature as multiple comparisons with a control (MCC). Independent steady-state simulations will be performed to compare the systems to the control. Two-stage procedures, based on the method of batch means, are presented to construct simultaneous lower one sided confidence intervals for/spl mu/i - /spl mu/k (i = 1, 2, . . ., k), each having prespecified (absolute or relative) half width 6. Under the assumption that the stochastic processes representing the evolution of the systems satisfy a functional central limit theorem, it can be shown that asymptotically (as /spl delta/ /spl rarr/ 0 with the size of the batches proportional to 1//spl delta//sup 2/), the joint probability that the confidence intervals simultaneously contain the /spl mu/i - /spl mu/k (i = 1, 2,..., k - 1) is at least 1 - /spl alpha/, where /spl alpha/ is prespecified by the user.


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
Billingsley, P. 1968. Convergence of Probability Measures. New York: John Wiley.
 
2
Damerdji, H. and M. K. Nakayama. 1996. Two-Stage Procedures for Multiple Comparisons with the Best in Steady-State Simulations. In preparation.
 
3
Dudewicz, E. J. and S. R. Dalal. 1983. Multiple comparisons with a control when variances are unknown and unequal. American Journal of Mathematics and Management Sciences 4:275-295.
 
4
Dudewicz, E. J. and J. S. Ramberg. 1972. Multiple comparison with a control: Unknown variances. Ann. Tech. Conf. Amer. Soc. Quality Control 26:483-488.
 
5
Dudewicz, E. J., J. S. Ramberg, and H. j. Chen. 1972. New tables for multiple comparisons with a control (unknown variances). Biometrische Zeitschrift 17:437-445.
 
6
Ethier, S. N. and T. G. Kurtz. 1986. Markov Processes: Characterization and Convergence. John Wiley, New York.
 
7
Glynn, P. W. 1990. Diffusion approximations. Chapter 4 of Handbooks in Operations Research and Management Science, Vol. 2, Stochastic Models, ed. D. Heyman and M. Sobel. Elsevier Science Publishers B. V. (North-Holland).
 
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Newman, C. M. and A. L. Wright. 1981. An invariance principle for certain dependent sequences. Annals of Probability 9:671-675.
 
13
Rinott, Y. 1978. On two-stage selection procedures and related probability-inequalities. Communications in Statistics--Theory and Methods A7:799- 811.
 
14
Tamhane, Y. 1977. Multiple comparisons in model i: One-way ANOVA with unequal variances. Communications in Statistics--Theory and Methods A6:15-32.
 
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Wilcox, R. R. 1984. A table for Rinott's selection procedure. Journal of Quality Technology 16:97- 100.

Collaborative Colleagues:
Halim Damerdji: colleagues
Marvin K. Nakayama: colleagues