Adjustments to Computer Models via Projected Kernel Calibration

DOI10.1137/17M1128769zbMath1430.62261arXiv1705.03422OpenAlexW2963876824WikidataQ127945253 ScholiaQ127945253MaRDI QIDQ5237166

Rui Tuo

Publication date: 17 October 2019

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1705.03422

zbMATH Keywords

computer experimentssemiparametric methodsuncertainty quantification

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Applications of statistics in engineering and industry; control charts (62P30) Foundations and philosophical topics in statistics (62A01)

Related Items (8)

Penalized Projected Kernel Calibration for Computer Models ⋮ Bayesian Projected Calibration of Computer Models ⋮ Fast Calibration for Computer Models with Massive Physical Observations ⋮ Calibration of Inexact Computer Models with Heteroscedastic Errors ⋮ A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models ⋮ On the Improved Rates of Convergence for Matérn-Type Kernel Ridge Regression with Application to Calibration of Computer Models ⋮ Calibration for computer experiments with binary responses and application to cell adhesion study ⋮ Effective model calibration via sensible variable identification and adjustment with application to composite fuselage simulation

Uses Software

• gss

Cites Work

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• Efficient calibration for imperfect computer models
• Calibrating a large computer experiment simulating radiative shock hydrodynamics
• Interpolation of spatial data. Some theory for kriging
• The design and analysis of computer experiments.
• Optimal global rates of convergence for nonparametric regression
• Introduction to empirical processes and semiparametric inference
• Bayesian Calibration of Computer Models
• Computer Model Calibration Using High-Dimensional Output
• Objective Bayesian Analysis of Spatially Correlated Data
• Asymptotic Statistics
• Orthogonal Gaussian process models
• Combining Field Data and Computer Simulations for Calibration and Prediction
• Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems
• A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties

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