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Simulation Designs for the Estimation of Quadratic Response Surface Gradients in the Presence of Model Misspecification

Authors:

Joan M. Donohue,

Ernest C. Houck,

Raymond H. MyersAuthors Info & Claims

Management Science, Volume 41, Issue 2

Pages 244 - 262

Published: 01 February 1995 Publication History

Abstract

This article considers the construction of simulation designs for the ordinary least squares estimation of second-order metamodels. Two premises underlie the development of these experimental strategies. First it is assumed that the postulated metamodel may be misspecified due to the true model structure being of third-order. It is therefore important that the locations of the simulation experiments be specified to provide protection against bias, as well as variance, in the estimation of metamodel parameters. The second premise is based on the observation that, in many applications of metamodels, functions of the fitted model coefficients such as the slope gradients are of greater interest than the response function. The integrated mean squared error of slopes design criterion that is implemented here addresses both premises. This criterion finds application in various optimum seeking methods and sensitivity analysis procedures. Combinations of four important classes of response surface designs and three pseudorandom number assignment strategies constitute the basis structure of the simulation designs studied. The performance of these simulation designs is evaluated and, subsequently, compared to a similar set of experimental plans that have as their focus the estimation of the response function.

References

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Digital Library

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Donohue, J. M., "The Use of Correlated Simulation Experiments in Response Surface Optimization," Ph.D. dissertation, Department of Management Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, 1988.

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Digital Library

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Donohue, J. M., E. C. Houck and R. H. Myers, "Simulation Designs for Controlling Second-Order Bias in First-Order Response Surfaces," Oper. Res., 41 (1993), 880-902.

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Digital Library

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Cited By

Chen XAnkenman BNelson B(2012)The effects of common random numbers on stochastic kriging metamodelsACM Transactions on Modeling and Computer Simulation10.1145/2133390.213339122:2(1-20)Online publication date: 30-Mar-2012
https://dl.acm.org/doi/10.1145/2133390.2133391
Chen XAnkenman BNelson BYücesan E(2010)Common random numbers and stochastic krigingProceedings of the Winter Simulation Conference10.5555/2433508.2433620(947-956)Online publication date: 5-Dec-2010
https://dl.acm.org/doi/10.5555/2433508.2433620
Brekelmans RDriessen LHamers HHertog D(2005)Gradient Estimation Schemes for Noisy FunctionsJournal of Optimization Theory and Applications10.1007/s10957-005-5496-2126:3(529-551)Online publication date: 1-Sep-2005
https://dl.acm.org/doi/10.1007/s10957-005-5496-2
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Simulation Designs for the Estimation of Quadratic Response Surface Gradients in the Presence of Model Misspecification

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Published In

cover image Management Science

Management Science Volume 41, Issue 2

February 1995

188 pages

ISSN:0025-1909

Issue’s Table of Contents

Publisher

INFORMS

Linthicum, MD, United States

Publication History

Published: 01 February 1995

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Cited By

Chen XAnkenman BNelson B(2012)The effects of common random numbers on stochastic kriging metamodelsACM Transactions on Modeling and Computer Simulation10.1145/2133390.213339122:2(1-20)Online publication date: 30-Mar-2012
https://dl.acm.org/doi/10.1145/2133390.2133391
Chen XAnkenman BNelson BYücesan E(2010)Common random numbers and stochastic krigingProceedings of the Winter Simulation Conference10.5555/2433508.2433620(947-956)Online publication date: 5-Dec-2010
https://dl.acm.org/doi/10.5555/2433508.2433620
Brekelmans RDriessen LHamers HHertog D(2005)Gradient Estimation Schemes for Noisy FunctionsJournal of Optimization Theory and Applications10.1007/s10957-005-5496-2126:3(529-551)Online publication date: 1-Sep-2005
https://dl.acm.org/doi/10.1007/s10957-005-5496-2
Angün EKleijnen JHertog DGürkan GSnowdon JCharnes J(2002)Recent advances in simulation optimizationProceedings of the 34th conference on Winter simulation: exploring new frontiers10.5555/1030453.1030506(377-383)Online publication date: 8-Dec-2002
https://dl.acm.org/doi/10.5555/1030453.1030506
Barton R(1998)Simulation metamodelsProceedings of the 30th conference on Winter simulation10.5555/293172.293221(167-176)Online publication date: 1-Dec-1998
https://dl.acm.org/doi/10.5555/293172.293221
Donohue JLilegdon WGoldsman D(1995)The use of variance reduction techniques in the estimation of simulation metamodelsProceedings of the 27th conference on Winter simulation10.1145/224401.224462(194-200)Online publication date: 1-Dec-1995
https://dl.acm.org/doi/10.1145/224401.224462

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