research-article

Control variate technique: a constructive approach

Authors:

Tarik Borogovac,

Pirooz VakiliAuthors Info & Claims

WSC '08: Proceedings of the 40th Conference on Winter Simulation

Pages 320 - 327

Published: 07 December 2008 Publication History

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Abstract

The technique of control variates requires that the user identify a set of variates that are correlated with the estimation variable and whose means are known to the user. We relax the known mean requirement and instead assume the means are to be estimated. We argue that this strategy can be beneficial in parametric studies, analyze the properties of controlled estimators, and propose a class of generic and effective controls in a parametric estimation setting. We discuss the effectiveness of the estimators via analysis and simulation experiments.

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Borogovac, T., and P. Vakili. 2008. Database monte carlo approach to effective control variates. Technical report, Boston University College of Engineering.

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Zhao, G., T. Borogovac, and P. Vakili. 2007a. Efficient estimation of option price and price sensitivities via structured database monte carlo (SDMC). In Proceedings of the 2007 Winter Simulation Conference, ed. S. G. Henderson, B. Biller, M. H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, 984--991.

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Zhao, G., T. Borogovac, and P. Vakili. 2007b. Structured database monte carlo: A new strategy for efficient simulation. Technical report, Boston University College of Engineering.

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Zhao, G., Y. Zhou, and P. Vakili. 2006. A new efficient simulation strategy for pricing path -- dependent options. In Proceedings of the 2006 Winter Simulation Conference, ed. L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, 703--710.

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Cited By

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Feng MStaum J(2021)Green Simulation with Database Monte CarloACM Transactions on Modeling and Computer Simulation10.1145/342933631:1(1-26)Online publication date: 21-Jan-2021
https://dl.acm.org/doi/10.1145/3429336
Feng MStaum JHuschka TChick S(2016)Green simulation with database Monte CarloProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042241(1108-1118)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042241
Rosenbaum IStaum J(2015)Database monte carlo for simulation on demandProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888696(679-688)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888696
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Control variate technique: a constructive approach
1. Mathematics of computing
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    1. Functional analysis
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Information

Published In

WSC '08: Proceedings of the 40th Conference on Winter Simulation

December 2008

3189 pages

ISBN:9781424427086

Editors:
Scott Mason
University of Arkansas
,
Ray Hill
Air Force Institute of Technology
,
Lars Mönch
University of Hagen
,
Oliver Rose
University of Dresden
,
General Chair:
Thomas Jefferson
Intel Corporation
,
Program Chairs:
John W. Fowler
Arizona State University
,
Ricki G. Ingalls
Oklahoma State University

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Winter Simulation Conference

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Published: 07 December 2008

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WSC08

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IIE
INFORMS-SIM
ASA
IEEE/SMC
SIGSIM
NIST
(SCS)

WSC08: Winter Simulation Conference

December 7 - 10, 2008

Florida, Miami

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WSC '08 Paper Acceptance Rate 249 of 304 submissions, 82%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Cited By

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Feng MStaum J(2021)Green Simulation with Database Monte CarloACM Transactions on Modeling and Computer Simulation10.1145/342933631:1(1-26)Online publication date: 21-Jan-2021
https://dl.acm.org/doi/10.1145/3429336
Feng MStaum JHuschka TChick S(2016)Green simulation with database Monte CarloProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042241(1108-1118)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042241
Rosenbaum IStaum J(2015)Database monte carlo for simulation on demandProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888696(679-688)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888696
Tang XVakili PYücesan E(2010)Importance sampling for parametric estimationProceedings of the Winter Simulation Conference10.5555/2433508.2433838(2666-2677)Online publication date: 5-Dec-2010
https://dl.acm.org/doi/10.5555/2433508.2433838
Borogovac TSun NVakili PYücesan E(2010)Control variates for sensitivity estimationProceedings of the Winter Simulation Conference10.5555/2433508.2433834(2629-2641)Online publication date: 5-Dec-2010
https://dl.acm.org/doi/10.5555/2433508.2433834
Staum JDunkin AIngalls R(2009)Better simulation metamodelingWinter Simulation Conference10.5555/1995456.1995478(119-133)Online publication date: 13-Dec-2009
https://dl.acm.org/doi/10.5555/1995456.1995478

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Matrix variate slash distribution

Derivative estimation with known control-variate variances

Quantity properties of variate and coefficient in errors-in-variables model under Gaussian noise

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