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Variance reduction for simulation practitioners

Author:

Barry L. NelsonAuthors Info & Claims

WSC '87: Proceedings of the 19th conference on Winter simulation

Pages 43 - 51

https://doi.org/10.1145/318371.318382

Published: 01 December 1987 Publication History

Abstract

A comprehensive guide to applying three well-known variance reduction techniques is given, including point and interval estimators, software requirements, and guidelines for experiment design.

References

[1]

Anonuevo, R. and B.L. Nelson (1986). Automated Estimation and Vari~mce Reduction for Steady-State Simulations. Proceedings of the 1986 Winter Simulation Conference, 871-875.

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Bratley, P., B.L, Fox, and L,E. Schrage (1983). A Guide to Simuh~tion. Springer-Verlag, NY.

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Clark, G.M. and W. Yang (1986). A Bonferroni Selection Procedure When Using Common Random Numbers with Unknown Variances. Proceedings of the 1986 Winter Simulation Conference, 313-315.

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Dudewicz, E.J. and D.R. Dalai (1975). Allocation of Observations in Ranking and Selection with Unequal Variances. Sankhya 37, 28-78.

[5]

Fishman, G.S. (1978). Grouping Observations in Digital Simulation. Management Science 24, 510-521.

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Goldsman, D. (1983). Ranking and Selection in Simulation. Proceedings of the 1983 Winter Simulation Conference, 387-393.

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Kleijnen, J.P.C. (1974). Statistical Techniques in Simulation, Part I. Marcel Dekker, NY.

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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.

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Law, A.M. and W.D. Kelton (1982). Simulation Modeling and Analysis. McGraw-Hill, NY.

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[10]

Nelson, B.L. (1985). A Decomposition Approach to Variance Reduction. Proceedings of the 1985 Winter Simulation Conference, 23-32.

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Nelson, B.L. (1986), Batch Size Effects on the Efficiency of Control Variates in Simulation. Working Paper Series No. 1986-001, Dept. of Industrial and Systems Engineering, Ohio State University.

[12]

Nelson, B.L. (1987a). A Perspective on Variance Reduction in Dynamic Simulation Experiments. Communications in Statistics - Simulation and Computation B 16, in press.

[13]

Nelson, B.L. (1987b). Some Properties of Simulation Interval Estimators Under Dependence Induction. Operations Research Letters 6, in press.

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Neter, J. and W. Wasserman (1974). Applied Linear Statistical Models. Irwin, Homewood, IL.

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Nozari, A., S.F. Arnold and C.D. Pegden (1984). Control Variates for Multipopulation Simulation Experiments. liE Transactions 16, 159-169.

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Porta Nova, A.M.O. and j.R. Wilson (1986). Using Control Variates to Estimate Multiresponse Simulation Metamodels. Proceedings of the 1986 Winter Simulation Conference, 326-334.

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Schmeiser, B. (1982). Batch Size Effects in "the Analysis of Simulation Output. Operations Research 30, 556-568.

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Schruben, L.W. and B.H. Margolin (1978). Pseudorandom Number Assignment in Statistically Designed Simulation and Distribution Sampling Experiments. Journal of the American Statistical Association 73, 504-525.

[19]

Tew, J.D. and J.R. Wilson (1985). Validation of Correlation Induction Strategies for Simulation Experiments. Proceedings of the 1985 Winter Simulation Conference, 190-195.

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Venkatraman, S. and J.R. Wilson (1986). The Efficiency of Control Variates in Multiresponse Simulation. Operations Research Letters 5, 37-42.

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Wilson, J.R. (1984). Variance Reduction Techniques for Digital Simulation. American Journal of Mathematical and Management Sciences 4, 277-312.

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Yang, W. (1985). A Bonferroni Selection Procedure when using Common Random Numbers with Unknown Variances. Unpublished M.S. thesis, Dept. of Industrial and Systems Engineering, Ohio State University.

Cited By

Scheidegger ABanerjee APereira TJohansson BJain S(2018)Uncertainty quantification in simulation modelsProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320712(1599-1610)Online publication date: 9-Dec-2018
https://dl.acm.org/doi/10.5555/3320516.3320712
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
Rosenbaum IStaum J(2015)Database Monte Carlo for simulation on demand2015 Winter Simulation Conference (WSC)10.1109/WSC.2015.7408206(679-688)Online publication date: Dec-2015
https://doi.org/10.1109/WSC.2015.7408206
Show More Cited By

Index Terms

Variance reduction for simulation practitioners
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
2. General and reference
  1. Document types
    1. Surveys and overviews

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Information

Published In

cover image ACM Conferences

WSC '87: Proceedings of the 19th conference on Winter simulation

December 1987

963 pages

ISBN:0911801324

DOI:10.1145/318371

Editors:
Arne Thesen
Univ. of Wisconsin-Madison
,
Hank Grant
FACTROL, Inc.
,
W. David Kelton
Univ. of Minnesota

Copyright © 1987 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 1987

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Cited By

Scheidegger ABanerjee APereira TJohansson BJain S(2018)Uncertainty quantification in simulation modelsProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320712(1599-1610)Online publication date: 9-Dec-2018
https://dl.acm.org/doi/10.5555/3320516.3320712
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
Rosenbaum IStaum J(2015)Database Monte Carlo for simulation on demand2015 Winter Simulation Conference (WSC)10.1109/WSC.2015.7408206(679-688)Online publication date: Dec-2015
https://doi.org/10.1109/WSC.2015.7408206
Nelson B(2011)Thirty years of "batch size effects"Proceedings of the Winter Simulation Conference10.5555/2431518.2431561(393-400)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2431561
Nelson B(2011)Thirty years of “Batch Size Effects”Proceedings of the 2011 Winter Simulation Conference (WSC)10.1109/WSC.2011.6147766(393-400)Online publication date: Dec-2011
https://doi.org/10.1109/WSC.2011.6147766
Anderson NEvans GBiles W(2006)Simulation optimization of logistics systems through the use of variance reduction techniques and criterion modelsEngineering Optimization10.1080/0305215060057428338:4(441-460)Online publication date: Jun-2006
https://doi.org/10.1080/03052150600574283
Crawford JGallwey T(2000)Bias and variance reduction in computer simulation studiesEuropean Journal of Operational Research10.1016/S0377-2217(99)00178-2124:3(571-590)Online publication date: Aug-2000
https://doi.org/10.1016/S0377-2217(99)00178-2
Grajal JAsensio A(1999)Multiparametric importance sampling for simulation of radar systemsIEEE Transactions on Aerospace and Electronic Systems10.1109/7.74568635:1(123-137)Online publication date: Jan-1999
https://doi.org/10.1109/7.745686
L'Ecuyer PSadowski DSeila A(1994)Efficiency improvement and variance reductionProceedings of the 26th conference on Winter simulation10.5555/193201.193994(122-132)Online publication date: 11-Dec-1994
https://dl.acm.org/doi/10.5555/193201.193994
L'Ecuyer P(1994)Efficiency improvement and variance reductionProceedings of Winter Simulation Conference10.1109/WSC.1994.717089(122-132)Online publication date: 1994
https://doi.org/10.1109/WSC.1994.717089
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