Robust Kriging models in computer experiments

Taejin Park, Bongjin Yum, Ying Hung, Young-Seon Jeong and Myong K Jeong
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Taejin Park: Department of Industrial and Systems Engineering, KAIST, Daejeon, Republic of Korea
Bongjin Yum: Department of Industrial and Systems Engineering, KAIST, Daejeon, Republic of Korea
Ying Hung: Department of Statistics and Biostatistics, Rutgers University, Piscataway, USA
Young-Seon Jeong: Department of Industrial Engineering, Chonnam National University, Gwangju, Republic of Korea
Myong K Jeong: Department of Industrial and Systems Engineering, Rutgers University, Piscataway, USA

Journal of the Operational Research Society, 2016, vol. 67, issue 4, 644-653

Abstract: In the Gaussian Kriging model, errors are assumed to follow a Gaussian process. This is reasonable in many cases, but such an assumption is not appropriate for the situations when outliers are present. Large prediction errors may occur in those cases and more robust estimation is critical. In this article, we propose a robust estimation of Kriging parameters by utilizing other loss functions rather than classical L2 criterion. To make these estimators more robust to outliers, the L1 in this article. Mathematical programming formulations are developed upon the idea of support vector machine. A machining experiment data are analysed to verify usefulness of the proposed method.

Date: 2016
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