The use of variance reduction techniques in the estimation of simulation metamodels
Pages 194 - 200
Abstract
Variance reduction techniques can be useful strategies for improving the estimates of simulation metamodel coefficients. Depending upon the goals of the experimenter, the type of metamodel being estimated, and the characteristics of the system being simulated, an appropriate variance reduction technique can be applied. This paper provides a review of recent research that investigates the application of variance reduction techniques in the simulation metamodeling context. One strategy, Schruben and Margolin's (1978) assignment rule, which utilizes a combination of antithetic and common random number streams, is found to be a particularly useful variance reduction technique for the estimation of simulation metamodels.
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Index Terms
- The use of variance reduction techniques in the estimation of simulation metamodels
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Published In
December 1995
1493 pages
ISBN:0780330188
- Chairmen:
- William R. Lilegdon,
- David Goldsman,
- Editors:
- Christos Alexopoulos,
- Keebom Kang
Sponsors
- IIE: Institute of Industrial Engineers
- IEEE-SMCS: Systems, Man & Cybernetics Society
- INFORMS/CS: Computer Science TC
- ASA: American Statistical Association
- ACM: Association for Computing Machinery
- SCS: Society for Computer Simulation
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- IEEE-CS: Computer Society
- NIST: National Institute of Standards & Technology
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IEEE Computer Society
United States
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Published: 01 December 1995
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Overall Acceptance Rate 3,247 of 4,890 submissions, 66%
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