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Bootstrap methods in computer simulation experiments

Author:

Russell C. H. ChengAuthors Info & Claims

WSC '95: Proceedings of the 27th conference on Winter simulation

Pages 171 - 177

https://doi.org/10.1145/224401.224459

Published: 01 December 1995 Publication History

Abstract

We critically review the work that has been done in applying basic, smoothed and parametric bootstrap methods to simulation experiments. We develop a framework to classify bootstrap methods in this context and use it to compare various bootstrap schemes. Most bootstrap methods are hard to analyse theoretically. An exception is the parametric case for which a detailed analysis can be carried out. An interesting result in this case is that, whereas in standard statistical experiments bootstrap samples give only information about the variance of a statistic and not its mean, this turns out not to be so in simulation experiments. Thus parametric bootstrap samples can be advantageously included in estimates of the responses of interest.

References

[1]

Banks, D.L. 1989. Improving the Bayesian bootstrap. Unpublished paper. Dept of Pure Mathematics and Mathematical Statistics, Cambridge University.

[2]

Barton, R.R. and Schruben, L.W. 1993 Uniform and bootstrap resampling of empirical distributions. In Proceedings of the 1993 Winter Simulation Conference (ed. G.W. Evans, M. Mollaghasemi, E.C. Russell and W.E. Biles), IEEE Piscataway, New Jersey, 503-508.

Digital Library

[3]

Bratley, P., Fox, B.L. and Schrage, L.E. 1983. A Guide to Simulation. New York: Springer-Verlag.

Digital Library

[4]

Cheng, R.C.H. 1994. Selecting input models. In Proceedings of the l Og~ Winter Simulation Conference (ed. J.D. Tew, S. Manivannan, D.A. Sadowski and A.F. Seila), IEEE Piseataway, New Jersey, 184-191.

Digital Library

[5]

Cheng, R.C.H. and Holland, W. 1995a. The effect of input parameters on the variability of simulation output. In Proceedings of UKSS'95, The Second Conference of the U.K. Simulation Society (ed. R.C.H. Cheng and R.J. Pooley), UK Simulation Society, EUCS Reprographics, Edinburgh Univ., 29- 36.

[6]

Cheng, R.C.H. and Holland, W. I995b. The sensitivity of computer simulation experiments to errors in input data. Accepted for SAMO95 International Symposium, Sept. 1995, Belgirate, Italy.

[7]

DiCiccio, T.J. and Romano, J.P. 1988. A review of bootstrap confidence intervals. J. R. Statist. ~ooc. B, 50, 338-354.

[8]

Efron, B. 1982. The jackknife, the bootstrap and other resampling plans. Volume 38 of CBMS-NSF Regional Conference Series in Applied Mathematics. SIAM.

[9]

Efron, B. and Tibshirani, R.J. 1993. An Introduction to the Bootstrap. New York and London: Chapman and Hall.

[10]

Hinkley D.J. 1988. Bootstrap methods. J. R. S~a~ist. Soc. B, 50, 321-337.

[11]

Kim, Y. B., Haddock, J. and Willemain, T. R. 1993a. The binary bootstrap: inference with correlated data. Commun. oCtatist.-Szmula., 22, 205-216.

[12]

Kim, Y.B., Willemain, T.R., Haddock, J. and Runger, G.C. 1993b. The threshold bootstrap: a new approach to simulation output analysis. In Proceedings of ~he 1993 W~nter Szmulation Conference (ed. G.W. Evans, M. Mollaghasemi, E.C. Russell and W.E. Biles), IEEE Piscataway, New Jersey, 498-502.

Digital Library

[13]

Nelson, B. 1990. Control-variate remedies, Opns Res. 38, 974-992.

Digital Library

[14]

Rubin, D.B. 1981. The Bayesian bootstrap. Ann. Statist. 9, 130-134.

[15]

Shiue, W.-K., Xu, C.-W. and Rea, C.B. 1993. Bootstrap confidence intervals for simulation outputs. J. Statzst. Compu~. Simul., 45, 249-255.

[16]

Shanker, A. and KeIton, W. D. 1994. Measuring output error due to input error in simulation: analysis of fitted vs. mixed empirical distributions for queues. To appear.

[17]

Silverman, B.W. and Young, G.A. 1987. The bootstrap: to smooth or not to smooth? Biome~rika, 74, 469-479.

[18]

Young, G.A. 1990. Alternative smoothed bootstraps. j.R. Sta~s~. Soc. B, 52,477-484.

[19]

Yiicesan, E. 1994. Nonparametric statistics in simulation analysis: a tutorial. (ed. J.D. Tew, S. Manivannan, D.A. Sadowski and A.F. Seila), IEEE Piscataway, New Jersey, 99-105.

Digital Library

Cited By

Romero-Silva RHurtado M(2017)Estimation of Quantile Confidence Intervals for Queueing Systems Based on the Bootstrap MethodologyApplied Computer Sciences in Engineering10.1007/978-3-319-66963-2_25(275-286)Online publication date: 29-Aug-2017
https://doi.org/10.1007/978-3-319-66963-2_25
Barton RRose OUhrmacher A(2012)Input uncertainty in outout analysisProceedings of the Winter Simulation Conference10.5555/2429759.2429766(1-12)Online publication date: 9-Dec-2012
https://dl.acm.org/doi/10.5555/2429759.2429766
Schruben LSingham D(2011)Agent based simulation output analysisProceedings of the Winter Simulation Conference10.5555/2431518.2431581(540-548)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2431581
Show More Cited By

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Bootstrap methods in computer simulation experiments

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Published In

cover image ACM Conferences

WSC '95: Proceedings of the 27th conference on Winter simulation

December 1995

1493 pages

ISBN:0780330188

Chairmen:
William R. Lilegdon
Pritsker Corp., West Lafayette, IN
,
David Goldsman
Georgia Institute of Technology, Atlanta
,
Editors:
Christos Alexopoulos
Georgia Institute of Technology, Atlanta
,
Keebom Kang
Naval Postgraduate School, Monterey, CA

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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SIGSIM
IEEE-CS
NIST

WSC95: 1995 Winter Simulation Conference

December 3 - 6, 1995

Virginia, Arlington, USA

Acceptance Rates

WSC '95 Paper Acceptance Rate 122 of 183 submissions, 67%;

Overall Acceptance Rate 3,247 of 4,890 submissions, 66%

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Cited By

Romero-Silva RHurtado M(2017)Estimation of Quantile Confidence Intervals for Queueing Systems Based on the Bootstrap MethodologyApplied Computer Sciences in Engineering10.1007/978-3-319-66963-2_25(275-286)Online publication date: 29-Aug-2017
https://doi.org/10.1007/978-3-319-66963-2_25
Barton RRose OUhrmacher A(2012)Input uncertainty in outout analysisProceedings of the Winter Simulation Conference10.5555/2429759.2429766(1-12)Online publication date: 9-Dec-2012
https://dl.acm.org/doi/10.5555/2429759.2429766
Schruben LSingham D(2011)Agent based simulation output analysisProceedings of the Winter Simulation Conference10.5555/2431518.2431581(540-548)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2431581
Lemaitre BSohrmann CMuche LHaase J(2011)Physical and Mathematical FundamentalsProcess Variations and Probabilistic Integrated Circuit Design10.1007/978-1-4419-6621-6_2(11-67)Online publication date: 8-Oct-2011
https://doi.org/10.1007/978-1-4419-6621-6_2
Virani SEtzkorn LGholston SFarrington PUtley DFortune JMcGregor J(2009)Investigation of domain effects on softwareProceedings of the 47th annual ACM Southeast Conference10.1145/1566445.1566496(1-5)Online publication date: 19-Mar-2009
https://dl.acm.org/doi/10.1145/1566445.1566496
Li PComerio MMaurino APaoli F(2009)Advanced Non-functional Property Evaluation of Web ServicesProceedings of the 2009 Seventh IEEE European Conference on Web Services10.1109/ECOWS.2009.8(27-36)Online publication date: 9-Nov-2009
https://dl.acm.org/doi/10.1109/ECOWS.2009.8
Rother DSapiro GPande V(2008)Statistical Characterization of Protein EnsemblesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2007.10615:1(42-55)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.1109/TCBB.2007.1061
Angus DLinde-Zwirble WTam SGhali JSabolinski MVillagra VWinkelmayer WWorcel M(2005)Cost-Effectiveness of Fixed-Dose Combination of Isosorbide Dinitrate and Hydralazine Therapy for Blacks With Heart FailureCirculation10.1161/CIRCULATIONAHA.105.563882112:24(3745-3753)Online publication date: 13-Dec-2005
https://doi.org/10.1161/CIRCULATIONAHA.105.563882
Lei SSmith M(2003)Evaluation of Several Nonparametric Bootstrap Methods to Estimate Confidence Intervals for Software MetricsIEEE Transactions on Software Engineering10.1109/TSE.2003.124530129:11(996-1004)Online publication date: 1-Nov-2003
https://dl.acm.org/doi/10.1109/TSE.2003.1245301
Cheng RRohrer MMedeiros DGrabau M(2001)Analysis of simulation experiments by bootstrap resamplingProceedings of the 33nd conference on Winter simulation10.5555/564124.564148(179-186)Online publication date: 9-Dec-2001
https://dl.acm.org/doi/10.5555/564124.564148
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