Additive Gaussian Process for Computer Models With Qualitative and Quantitative Factors
Xinwei Deng, Chunfang Devon Lin, Kaizhao Liu, Ronald Kerry Rowe · Technometrics · 2016
X. Denga, C. Devon Linb*, K.-W. Liuc & R. K. Roweda Department of Statistics, Virginia Tech, Blacksburg, Virginiab Department of Mathematics and Statistics, Queen's University, Kingston, Canadac School of Civil Engineering, Southwest Jiaotong University, Emeishan, Leshan, Sichuan, Chinad Department of Civil Engineering, Queen's University, Kingston, CanadaCONTACT C. Devon Lin [email protected] Department of Mathematics and Statistics, Queen's University, Kingston, ON K7L 3N6, CanadaColor versions of one or more of the figures in this article are available online at www.tandfonline.com/r/TECH.Supplementary materials for this article are available online. Please go to http://www.tandfonline.com/r/TECH.ABSTRACTComputer experiments with qualitative and quantitative factors occur frequently in various applications in science and engineering. Analysis of such experiments is not yet completely resolved. In this work, we propose an additive Gaussian process model for computer experiments with qualitative and quantitative factors. The proposed method considers an additive correlation structure for qualitative factors, and assumes that the correlation function for each qualitative factor and the correlation function of quantitative factors are multiplicative. It inherits the flexibility of unrestrictive correlation structure for qualitative factors by using the hypersphere decomposition, embracing more flexibility in modeling the complex systems of computer experiments. The merits of the proposed method are illustrated by several numerical examples and a real data application. Supplementary materials for this article are available online.