ACM Home Page
Please provide us with feedback. Feedback
Control variate techniques for monte carlo simulation: control variates techniques for monte carlo simulation
Full text PdfPdf (156 KB)
Source Winter Simulation Conference archive
Proceedings of the 35th conference on Winter simulation: driving innovation table of contents
New Orleans, Louisiana
SESSION: Advanced tutorials table of contents
Pages: 144 - 149  
Year of Publication: 2003
ISBN:0-7803-8132-7
Author
Roberto Szechtman  Naval Postgraduate School, Monterey, CA
Sponsors
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
NIST : National Institute of Standards and Technology
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
ACM: Association for Computing Machinery
(SCS) : The Society for Modeling and Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
Publisher
Winter Simulation Conference 
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 120,   Citation Count: 1
Additional Information:

abstract   references   cited by   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

In this paper we present an overview of classical results about the variance reduction technique of control variates. We emphasize aspects of the theory that are of importance to the practitioner, as well as presenting relevant applications.


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. 1999. Convergence of Probability Measures. 2nd ed. New York: Wiley.
 
2
 
3
Duffie, D. 1996. Dynamic Asset Pricing theory. 2nd ed. Princeton: Princeton University Press.
 
4
Glasserman, P. 2003. Monte Carlo Methods in Financial Engineering. New York: Springer-Verlag.
 
5
 
6
Lavenberg, S. S., and P. D. Welch. 1981. A perspective on the use of control variables to increase the efficiency of Monte Carlo simulations. Management Science 27:322--335.
 
7
Lavenberg, S. S., T. L. Moeller and P. D. Welch. 1982. Statistical results on control variates with application to queueing network simulation. Operations Research 30:182--202.
 
8
 
9
Loh, W. W. 1995. On the Method of Control Variates. Ph. D. Thesis. Department of Operations Research. Stanford University.
 
10
 
11
Rubinstein, R. Y., and R. Marcus. 1985. Efficiency of multivariate control variates in Monte Carlo simulation. Operations Research 33:661--677.
 
12