Efficient Calibration for Imperfect Computer Models
Methodology
2015-07-28 v1
Abstract
Many computer models contain unknown parameters which need to be estimated using physical observations. Kennedy and O'Hagan (2001) shows that the calibration method based on Gaussian process models proposed by Kennedy and O'Hagan (2001) may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Numerical examples show that the proposed method outperforms the existing ones.
Cite
@article{arxiv.1507.07280,
title = {Efficient Calibration for Imperfect Computer Models},
author = {Rui Tuo and C. F. Jeff Wu},
journal= {arXiv preprint arXiv:1507.07280},
year = {2015}
}