Computing the distribution function of a conditional expectation via Monte Carlo

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Computing the distribution function of a conditional expectation via Monte Carlo: discrete conditioning spaces

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Winter Simulation Conference archive
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2 table of contents

Phoenix, Arizona, United States

Pages: 1654 - 1663

Year of Publication: 1999

ISBN:0-7803-5780-9

Authors

Shing-Hoi Lee	Fixed-Income Research Group, Morgan Stanley Dean Witter & Co., 1585 Broadway, Manhattan, NY
Peter W. Glynn	Department of Engineering-Economic Systems and Operations Research, Stanford University, Stanford, CA

Sponsors

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

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 13, Citation Count: 7

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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	Billingsley, P. 1995. Probability and Measure 3rd-ed. New York: John Wiley and Sons.
	2	Bucklew, J. 1990. Large Deviation Techniques in Decision, Simulation and Estimation. New York: John Wiley and Sons.
	3	Duffie, D. 1996. Dynamic Asset Pricing Theory. 2nd ed. Princeton University Press, Princeton, New Jersey.
	4	Lee, S.H. and Glynn, P.W. 1999. Computing the Distribution Function of a Conditional Expectation via Monte Carlo. Submitted for publication.

CITED BY 7

	Samuel G. Steckley , Shane G. Henderson, Simulation input modeling: a kernel approach to estimating the density of a conditional expectation, Proceedings of the 35th conference on Winter simulation: driving innovation, December 07-10, 2003, New Orleans, Louisiana
	Shane G. Henderson, Input modeling: input model uncertainty: why do we care and what should we do about it?, Proceedings of the 35th conference on Winter simulation: driving innovation, December 07-10, 2003, New Orleans, Louisiana
	Russell R. Barton , Stephen E. Chick , Russell C. H. Cheng , Shane G. Henderson , Averill M. Law , Bruce W. Schmeiser , Lawrence M. Leemis , Lee W. Schruben , James R. Wilson, Panel discussion on current issues in input modeling: panel on current issues in simulation input modeling, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
	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
	Stephen E. Chick, Bayesian methods: bayesian methods for simulation, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
	Stephen E. Chick, Bayesian methods for discrete event simulation, Proceedings of the 36th conference on Winter simulation, December 05-08, 2004, Washington, D.C.
	Stephen E. Chick, Bayesian ideas and discrete event simulation: why, what and how, Proceedings of the 37th conference on Winter simulation, December 03-06, 2006, Monterey, California