Semi-parametric adjustment to computer models. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Wang, Yan; Tuo, Rui
• Is Part Of: Statistics. Volume 54:Issue 6(2020)
• Page Start: 1255
• Page End: 1275
• Publication Date: 2020-11-01
• Subjects: Computer experiments -- reproducing kernel Hilbert spaces -- prediction -- uncertainty quantification; 60G15; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2020.1862113
• Languages: English
• ISSNs: 0233-1888

Abstract

Abstract : Computer simulations are widely used in scientific exploration and engineering designs. However, computer outputs usually do not match the reality perfectly because the computer models are built under certain simplifications and approximations. When physical observations are also available, statistical methods can be applied to estimate the discrepancy between the computer output and the physical response. In this article, we propose a semi-parametric method for statistical adjustments to computer models. The proposed method is proven to enjoy nice theoretical properties. We use three numerical studies and a real example to examine the predictive performance of the proposed method. The results show that the proposed method outperforms existing methods.