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Screening for dispersion effects by sequential bifurcation

Screening for dispersion effects by sequential bifurcation
Screening for dispersion effects by sequential bifurcation
The mean of the output of interest obtained from a run of a computer simulation model of a system or process often depends on many factors; many times however only a few of these factors are important. Sequential bifurcation is a method that has been considered by several authors for identifying these important factors using as few runs of the simulation model as possible. In this paper, we propose a new sequential bifurcation procedure whose steps use a key stopping rule that can be calculated explicitly, something not available in the best methods previously considered. Moreover we show how this stopping rule can also be easily modified to efficiently identify those factors that are important in influencing the variability rather than the mean of the output. In empirical studies, the new method performs better than previously published fully sequential bifurcation methods in terms of achieving the prescribed Type I error and high power for detecting moderately large effects using fewer replications than earlier methods. To achieve this control for midrange effects, the new method sometimes requires more replications than other methods in the case where there are many very large effects
1049-3301
Ankenman, B.E.
405c4fe9-d9da-43f3-bb5b-ba2e4c8b1732
Cheng, R.C.H.
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Lewis, S.M.
a69a3245-8c19-41c6-bf46-0b3b02d83cb8
Ankenman, B.E.
405c4fe9-d9da-43f3-bb5b-ba2e4c8b1732
Cheng, R.C.H.
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Lewis, S.M.
a69a3245-8c19-41c6-bf46-0b3b02d83cb8

Ankenman, B.E., Cheng, R.C.H. and Lewis, S.M. (2015) Screening for dispersion effects by sequential bifurcation. ACM Transactions on Modeling and Computer Simulation, 25 (1). (doi:10.1145/2651364).

Record type: Article

Abstract

The mean of the output of interest obtained from a run of a computer simulation model of a system or process often depends on many factors; many times however only a few of these factors are important. Sequential bifurcation is a method that has been considered by several authors for identifying these important factors using as few runs of the simulation model as possible. In this paper, we propose a new sequential bifurcation procedure whose steps use a key stopping rule that can be calculated explicitly, something not available in the best methods previously considered. Moreover we show how this stopping rule can also be easily modified to efficiently identify those factors that are important in influencing the variability rather than the mean of the output. In empirical studies, the new method performs better than previously published fully sequential bifurcation methods in terms of achieving the prescribed Type I error and high power for detecting moderately large effects using fewer replications than earlier methods. To achieve this control for midrange effects, the new method sometimes requires more replications than other methods in the case where there are many very large effects

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More information

Accepted/In Press date: 2014
e-pub ahead of print date: 14 January 2015
Published date: 14 January 2015
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 372523
URI: http://eprints.soton.ac.uk/id/eprint/372523
ISSN: 1049-3301
PURE UUID: a5e9ea4c-7300-470d-bed0-cdf09bf77857

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Date deposited: 12 Dec 2014 15:24
Last modified: 15 Jul 2019 21:36

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