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Analysis methodology: are we done?
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Source Winter Simulation Conference archive
Proceedings of the 37th conference on Winter simulation table of contents
Orlando, Florida
PANEL SESSION: Analysis methodology A: analysis methodlogy reflection table of contents
Pages: 790 - 796  
Year of Publication: 2005
ISBN:0-7803-9519-0
Authors
Sigrün Andradóttir  Georgia Institute of Technology, Atlanta, GA
David Goldsman  Georgia Institute of Technology, Atlanta, GA
Lee W. Schruben  University of California, Berkeley, CA
Bruce W. Schmeiser  Purdue University, W. Lafayette, IN
Enver Yücesan  INSEAD, Fontainebleau Cedex, France
Publisher
Winter Simulation Conference 
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ABSTRACT

Since 1967, the Winter Simulation Conference has been a forum for the introduction of innovative approaches to effectively analyze discrete-event simulation experiments. The goal of this panel is to bring together key contributors to analysis methodology research in order to clarify areas that they think are essentially complete, and identify areas that need more work. In doing so, we hope to help provide direction to younger researchers looking for the "right" problems to work on.


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.

 
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Cario, M. C. and B. L. Nelson. 1997. Modeling and generating random vectors with arbitrary marginal distributions and correlation matrix. Technical Report, Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois.
 
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Conway, R. 1963. Some tactical problems in digital simulation. Management Science, 10: 47--61.
 
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Fishman, G. S. and L. S. Yarberry. 1997. An implementation of the batch means method. INFORMS Journal on Computing, 9(3): 296--310.
 
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Glasserman, P. 2004. Monte Carlo Methods in Financial Engineering, Springer. New York.
 
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Govind, N. 2004. Robust parameter design with imperfect experimental control of noise. Unpublished Ph.D. Dissertation, The Pennsylvania State University.
 
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Melamed, B. 1991. TES: a class of methods for generating autocorrelated uniform variates. ORSA Journal on Computing, 3:317--329.
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Wagner, M. A. F. and J. R. Wilson. 1996. Using univariate Bezier distributions to model simulation input processes. IIE Transactions, 28 (9): 699--712.
 
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Yarris, L., Computing resources available to Lab researchers, Berkeley Lab Currents, Lawrence Berkeley Labs, July 12, 1996.

Collaborative Colleagues:
Sigrün Andradóttir: colleagues
David Goldsman: colleagues
Lee W. Schruben: colleagues
Bruce W. Schmeiser: colleagues
Enver Yücesan: colleagues