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Kriging Models That Are Robust With Respect to Simulation Errors

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Author Info
Siem, A.Y.D.
Hertog, D. den (Tilburg University, Center for Economic Research)
Abstract

In the field of the Design and Analysis of Computer Experiments (DACE) meta-models are used to approximate time-consuming simulations. These simulations often contain simulation-model errors in the output variables. In the construction of meta-models, these errors are often ignored. Simulation-model errors may be magnified by the meta-model. Therefore, in this paper, we study the construction of Kriging models that are robust with respect to simulation-model errors. We introduce a robustness criterion, to quantify the robustness of a Kriging model. Based on this robustness criterion, two new methods to find robust Kriging models are introduced. We illustrate these methods with the approximation of the Six-hump camel back function and a real life example. Furthermore, we validate the two methods by simulating artificial perturbations. Finally, we consider the influence of the Design of Computer Experiments (DoCE) on the robustness of Kriging models.

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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2007-68.

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Date of creation: 2007
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Handle: RePEc:dgr:kubcen:200768

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C60 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - General

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  1. Stinstra, Erwin & den Hertog, Dick, 2008. "Robust optimization using computer experiments," European Journal of Operational Research, Elsevier, vol. 191(3), pages 816-837, December. [Downloadable!] (restricted)
  2. Kleijnen, J.P.C. & Beers, W.C.M. van, 2003. "Application-driven sequential designs for simulation experiments: kriging metamodeling," Discussion Paper 33, Tilburg University, Center for Economic Research. [Downloadable!]
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  1. Rennen, G., 2008. "Subset Selection from Large Datasets for Kriging Modeling," Discussion Paper 2008-26, Tilburg University, Center for Economic Research. [Downloadable!]
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