Radial basis function regularization for linear inverse problems with random noise

DOI10.1016/j.jmva.2012.09.007zbMath1277.62118OpenAlexW2018701189MaRDI QIDQ391523

Carlos E. Valencia, Ming Yuan

Publication date: 10 January 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2012.09.007

zbMATH Keywords

minimax rate of convergence

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric inference (62G99)

Related Items (3)

Physics constrained learning for data-driven inverse modeling from sparse observations ⋮ A meshfree method for inverse wave propagation using collocation and radial basis functions ⋮ Coupling RBF-based meshless method and Landweber iteration algorithm for approximating a space-dependent source term in a time fractional diffusion equation

Cites Work

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• Error estimates for interpolation by compactly supported radial basis functions of minimal degree
• Asymptotic equivalence of nonparametric regression and white noise
• Asymptotic equivalence of density estimation and Gaussian white noise
• Asymptotic equivalence for nonparametric generalized linear models
• Statistical learning theory: A primer
• Statistical properties of the method of regularization with periodic Gaussian reproducing kernel
• Nonparametric statistical inverse problems
• Radial Basis Functions
• Theory of Reproducing Kernels

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