n390898SE Academic Article uri icon

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.

published proceedings

  • SIAM/ASA Journal on Uncertainty Quantification

author list (cited authors)

  • Tuo, R.

publication date

  • January 1, 2019 11:11 AM