Fast Calibration for Computer Models with Massive Physical Observations

DOI10.1137/22m153673xarXiv2211.12731OpenAlexW4387149473MaRDI QIDQ6062240

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Publication date: 30 November 2023

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

Full work available at URL: https://arxiv.org/abs/2211.12731

zbMATH Keywords

massive dataPoisson samplingoptimal subsamplingweighted least squares calibration

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Nonparametric regression and quantile regression (62G08)

Cites Work

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• Efficient calibration for imperfect computer models
• The design and analysis of computer experiments
• Local polynomial regresssion estimators in survey sampling.
• Optimal subsampling for large-scale quantile regression
• Optimal subsampling for softmax regression
• Bayesian Calibration of Computer Models
• Measures, Integrals and Martingales
• A Framework for Controlling Sources of Inaccuracy in Gaussian Process Emulation of Deterministic Computer Experiments
• Large-Scale Machine Learning with Stochastic Gradient Descent
• Asymptotic Statistics
• Optimal Subsampling for Large Sample Logistic Regression
• On the Improved Rates of Convergence for Matérn-Type Kernel Ridge Regression with Application to Calibration of Computer Models
• Information-Based Optimal Subdata Selection for Big Data Linear Regression
• Adjustments to Computer Models via Projected Kernel Calibration
• Optimal Design of Experiments
• A Frequentist Approach to Computer Model Calibration
• Optimal subsampling for quantile regression in big data
• Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data
• Bayesian Projected Calibration of Computer Models

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