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Balancing bias and variance in the optimization of simulation models

Balancing bias and variance in the optimization of simulation models
Balancing bias and variance in the optimization of simulation models
We consider the problem of identifying the optimal point of an objective in simulation experiments where the objective is measured with error. The best stochastic approximation algorithms exhibit a convergence rate of n-1/6 which is somewhat different from the n-1/2 rate more usually encountered in statistical estimation. We describe some simple simulation experimental designs that emphasize the statistical aspects of the process. When the objective can be represented by a Taylor series near the optimum, we show that the best rate of convergence of the mean square error is when the variance and bias components balance each other. More specifically, when the objective can be approximated by a quadratic with a cubic bias, then the fastest decline in the mean square error achievable is n-2/3. Some elementary theory as well as numerical examples will be presented
0-7803-9519-0
485-490
Institute of Electrical and Electronic Engineers
Currie, Christine S.M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Cheng, Russell C.H.
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Kuhl, M.E.
Steiger, N.M.
Armstrong, F.B.
Joines, J.A.
Currie, Christine S.M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Cheng, Russell C.H.
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Kuhl, M.E.
Steiger, N.M.
Armstrong, F.B.
Joines, J.A.

Currie, Christine S.M. and Cheng, Russell C.H. (2005) Balancing bias and variance in the optimization of simulation models. Kuhl, M.E., Steiger, N.M., Armstrong, F.B. and Joines, J.A. (eds.) In Proceedings of the Winter Simulation Conference, 2005. Institute of Electrical and Electronic Engineers. pp. 485-490 . (doi:10.1109/WSC.2005.1574286).

Record type: Conference or Workshop Item (Paper)

Abstract

We consider the problem of identifying the optimal point of an objective in simulation experiments where the objective is measured with error. The best stochastic approximation algorithms exhibit a convergence rate of n-1/6 which is somewhat different from the n-1/2 rate more usually encountered in statistical estimation. We describe some simple simulation experimental designs that emphasize the statistical aspects of the process. When the objective can be represented by a Taylor series near the optimum, we show that the best rate of convergence of the mean square error is when the variance and bias components balance each other. More specifically, when the objective can be approximated by a quadratic with a cubic bias, then the fastest decline in the mean square error achievable is n-2/3. Some elementary theory as well as numerical examples will be presented

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More information

Published date: December 2005
Venue - Dates: 2005 Winter Simulation Conference, United States, 2005-12-04 - 2005-12-04
Organisations: Operational Research

Identifiers

Local EPrints ID: 43542
URI: https://eprints.soton.ac.uk/id/eprint/43542
ISBN: 0-7803-9519-0
PURE UUID: 2a86ac29-c4c2-4f56-98c0-2e6890269712
ORCID for Christine S.M. Currie: ORCID iD orcid.org/0000-0002-7016-3652

Catalogue record

Date deposited: 25 Jan 2007
Last modified: 14 Mar 2019 01:44

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Contributors

Editor: M.E. Kuhl
Editor: N.M. Steiger
Editor: F.B. Armstrong
Editor: J.A. Joines

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