Almost optimal differentiation using noisy data

DOI10.1006/jath.1996.0071zbMath0860.65010OpenAlexW2084424440MaRDI QIDQ1925089

Klaus Ritter

Publication date: 27 October 1996

Published in: Journal of Approximation Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jath.1996.0071

zbMATH Keywords

noisy datanumerical differentiationsmoothing spline methods

Mathematics Subject Classification ID

Numerical smoothing, curve fitting (65D10) Numerical differentiation (65D25)

Related Items (4)

Learning Curves for Gaussian Process Regression: Approximations and Bounds ⋮ Numerical differentiation of 2D functions from noisy data. ⋮ Average case \(L_\infty\)-approximation in the presence of Gaussian noise ⋮ Asymptotic analysis of the learning curve for Gaussian process regression

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