Calibration of computer models with multivariate output

Authors:
Rui Paulo;Gonzalo GarcíA-Donato;JesúS Palomo
Affiliations:
Department of Mathematics and CEMAPRE, ISEG, Technical University of Lisbon, Portugal;Department of Economics and Finance, Universidad de Castilla-La Mancha, Spain;Department of Business Administration (Finance), Rey Juan Carlos University, Spain
Venue:
Computational Statistics & Data Analysis
Year:
2012

Citing 4
Cited 1

Combining Field Data and Computer Simulations for Calibration and Prediction

SIAM Journal on Scientific Computing
Gaussian processes and limiting linear models

Computational Statistics & Data Analysis
Mechanism-based emulation of dynamic simulation models: Concept and application in hydrology

Computational Statistics & Data Analysis
Cases for the nugget in modeling computer experiments

Statistics and Computing

Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes

Journal of Multivariate Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

The problem of calibrating computer models that produce multivariate output is considered, with a particular emphasis on the situation where the model is computationally demanding. The proposed methodology builds on Gaussian process-based response-surface approximations to each of the components of the output of the computer model to produce an emulator of the multivariate output. This emulator is then combined in a statistical model involving field observations, which is then used to produce calibration strategies for the parameters of the computer model. The results of applying this methodology to a simulated example and to a real application are presented.