Subset Selection from Large Datasets for Kriging Modeling
AbstractWhen building a Kriging model, the general intuition is that using more data will always result in a better model. However, we show that when we have a large non-uniform dataset, using a uniform subset can have several advantages. Reducing the time necessary to fit the model, avoiding numerical inaccuracies and improving the robustness with respect to errors in the output data are some aspects which can be improved by using a uniform subset. We furthermore describe several new and current methods for selecting a uniform subset. These methods are tested and compared on several artificial datasets and one real life dataset. The comparison shows how the selected subsets affect different aspects of the resulting Kriging model. As none of the subset selection methods performs best on all criteria, the best method to choose depends on how the different aspects are valued. The comparison made in this paper can be used to facilitate the user in making a good choice.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2008-26.
Date of creation: 2008
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Design of computer experiments; dispersion problem; Kriging model; large non-uniform datasets; radial basis functions; robustness; space filling; subset selection; uniformity;
Find related papers by JEL classification:
- C0 - Mathematical and Quantitative Methods - - General
- C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-03-15 (All new papers)
- NEP-CMP-2008-03-15 (Computational Economics)
- NEP-ECM-2008-03-15 (Econometrics)
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- Siem, A.Y.D. & Hertog, D. den, 2007. "Kriging Models That Are Robust With Respect to Simulation Errors," Discussion Paper 2007-68, Tilburg University, Center for Economic Research.
- Erkut, Erhan & Neuman, Susan, 1989. "Analytical models for locating undesirable facilities," European Journal of Operational Research, Elsevier, vol. 40(3), pages 275-291, June.
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