Descriptive sampling

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Descriptive sampling: an improvement over Latin hypercube sampling

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Winter Simulation Conference archive
Proceedings of the 29th conference on Winter simulation table of contents

Atlanta, Georgia, United States

Pages: 230 - 233

Year of Publication: 1997

ISBN:0-7803-4278-X

Author

Eduardo Saliby

COPPEAD/UFRJ, Universidade Federal do Rio de Janeiro, Caixa Postal 68514, 21949-900, Rio de Janeiro, RJ, Brazil

Sponsors

IEEE-CS : Computer Society
IEEE-SMCS : Systems, Man & Cybernetics Society
ACM: Association for Computing Machinery
INFORMS/CS : Computer Science TC
SIGSIM: ACM Special Interest Group on Simulation and Modeling
SCS : Society for Computer Simulation
ASA : American Statistical Association
IEEE : Institute of Electrical and Electronics Engineers

Publisher

IEEE Computer Society Washington, DC, USA

Bibliometrics

Downloads (6 Weeks): 4, Downloads (12 Months): 26, Citation Count: 3

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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	Iman, R. L., and W. J. Conover. 1980. Small Sample Sensitivity Analysis Techniques for Computer Models, with an Application to Risk Assessment. Communications in Statistics: Theory and Methods A 9: 1749-l 874.
	2	Iman, R. L., and W. J. Conover. 1982. A Distributionfree Approach to Inducing Rank Correlation Among Input Variables. Communications in Statistics B 11: 311-334.
	3	Iman, R. L., and J. C. Helton. 1991. The Repeatability of Uncertainty and Sensitivity Analyses for Complex Probabilistic Risk Assessments. Risk Analysis 11: 591-606.
	4	Loh, W. L. 1996. On Latin Hypercube Sampling. The Annals of Statistics 24: 2058-2080.
	5	McKay, M. D., W. J. Conover and R. J. Beckman. 1979. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics 2 1: 239-245.
	6	Owen, A. B. 1992. A Central Limit Theorem for Latin Hypercube Sampling. Journal of the Royal Statistical Society Ser. B 54: 54 l-55 I.
	7	John S. Ramberg , Bruce W. Schmeiser, An approximate method for generating symmetric random variables, Communications of the ACM, v.15 n.11, p.987-990, Nov. 1972 [doi>10.1145/355606.361888]
	8	Saliby, E. 1980. A Reappraisal of Some Simulation Fundamentals. Ph.D. Thesis, University of Lancaster.
	9	Saliby, E. 1989. Rethinking Simulation: Descriptive Sampling. Sao Paulo: Atlas/EDUFRJ. (In Portuguese).
	10	Saliby, E. 1990. Descriptive Sampling: A Better Approach to Monte Carlo Simulation. Journal of the Operational Research Society 41: 1133-l 142.
	11	Saliby, E., and R. J. Paul. 1993. Implementing Descriptive Sampling in Three-Phase Discrete Event Simulation Models. Journal of the Operational Research Society 44: 147- 160.
	12	Michael Stein, Large sample properties of simulations using latin hypercube sampling, Technometrics, v.29 n.2, p.143-151, May 1, 1987 [doi>10.2307/1269769]

CITED BY 3

	Jeremy Staum , Samuel Ehrlichman , Vadim Lesnevski, New simulation methodology for finance: work reduction in financial simulations, Proceedings of the 35th conference on Winter simulation: driving innovation, December 07-10, 2003, New Orleans, Louisiana
	Eduardo Saliby , Flavio Pacheco, financial derivatives and real options: an empirical evaluation of sampling methods in risk analysis simulation: quasi-monte carlo, descriptive sampling, and Latin Hypercube sampling, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
	Eduardo Saliby , Jaqueline T. M. Marins , Josete F. dos Santos, Out-of-the-money monte carlo simulation option pricing: the joint use of importance sampling and descriptive sampling, Proceedings of the 37th conference on Winter simulation, December 04-07, 2005, Orlando, Florida