Discrete approximation by variational vector splines for noisy data
type: Item
instance of: scholarly article
title: Discrete approximation by variational vector splines for noisy data
zbMATH Open document ID: 1180.65018
published in: Mathematics and Computers in Simulation
publication date: 2009-10-01T00:00:00Z
review text: The authors deal with the problem of constructing and/or approximating a curve or surface from a scattered data set. Their purpose is to adapt the theory of the discrete variational splines relative to sets of data perturbed by a random noise in order to obtain an almost sure convergence for these splines. They also present some numerical and graphical examples in order to prove the efficiency of the method.
Mathematics Subject Classification ID: 65D17
Mathematics Subject Classification ID: 65D10
Mathematics Subject Classification ID: 65D05
Mathematics Subject Classification ID: 65D07
Mathematics Subject Classification ID: 41A15
Mathematics Subject Classification ID: 41A29
zbMATH DE Number: 5610220
zbMATH Keywords: curves and surfaces approximation
zbMATH Keywords: noisy data
zbMATH Keywords: variational splines
zbMATH Keywords: almost sure convergence
zbMATH Keywords: scattered data
zbMATH Keywords: numerical and graphical examples
author: A. Kouibia
author: M. Pasadas
reviewed by: Delfina Roux
MaRDI profile type: Publication
full work available at URL: j.matcom.2009.04.022
OpenAlex ID: W2053140140
cites work: Q4652401
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cites work: Approximation by discrete variational splines
cites work: Approximation by smoothing variational vector splines for noisy data
cites work: Q3988921
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cites work: Error bounds for derivative estimates based on spline smoothing of exact or noisy data
cites work: Convergence rates for multivariate smoothing spline functions
cites work: Q3996207
DOI: 10.1016/J.MATCOM.2009.04.022
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description: scientific article; zbMATH DE number 5610220