Adjustments to Computer Models via Projected Kernel Calibration Academic Article

abstract

• Copyright by SIAM and ASA. Identification of model parameters in computer simulations is an important topic in computer ex- periments. We propose a new method, called the projected kernel calibration method, to estimate these model parameters. The proposed method is proven to be asymptotic normal and semipara- metric efficient. As a frequentist method, the proposed method is as efficient as the L2 calibration method proposed by Tuo and Wu [Ann. Statist., 43 (2015), pp. 2331{2352]. On the other hand, the proposed method has a natural Bayesian version, which the L2 method does not have. This Bayesian version allows users to calculate the credible region of the calibration parameters without using a large sample approximation. We also show that the inconsistency problem of the calibration method proposed by Kennedy and O'Hagan [J. R. Stat. Soc. Ser. B. Stat. Methodol., 63 (2001), pp. 425{464] can be rectified by a simple modification of the kernel matrix.

authors

• Tuo, Rui

published proceedings

• SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION

author list (cited authors)

• Tuo, R.

citation count

• 14

complete list of authors

• Tuo, Rui

publication date

• January 2019

publisher

• Society for Industrial & Applied Mathematics (SIAM)  Publisher

published in

• SIAM/ASA Journal on Uncertainty Quantification  Journal

keywords

• Computer Experiments
• Semiparametric Methods
• Uncertainty Quantification

Digital Object Identifier (DOI)

• 10.1137/17M1128769

start page

• 553

end page

• 578

volume

• 7

issue

• 2

URL

• http://dx.doi.org/10.1137/17m1128769