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• W2998556659 abstract "Kernel ridge regression is an important nonparametric method for estimating smooth functions. We introduce a new set of conditions, under which the actual rates of convergence of the kernel ridge regression estimator under both the L_2 norm and the norm of the reproducing kernel Hilbert space exceed the standard minimax rates. An application of this theory leads to a new understanding of the Kennedy-O'Hagan approach for calibrating model parameters of computer simulation. We prove that, under certain conditions, the Kennedy-O'Hagan calibration estimator with a known covariance function converges to the minimizer of the norm of the residual function in the reproducing kernel Hilbert space." @default.
• W2998556659 created "2020-01-10" @default.
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• W2998556659 date "2020-01-01" @default.
• W2998556659 modified "2023-09-24" @default.
• W2998556659 title "On the Improved Rates of Convergence for Mat'ern-type Kernel Ridge Regression, with Application to Calibration of Computer Models" @default.
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• W2998556659 doi "https://doi.org/10.48550/arxiv.2001.00152" @default.
• W2998556659 hasPublicationYear "2020" @default.
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