Fourier series approximation of separable models
type: Item
instance of: scholarly article
title: Fourier series approximation of separable models
zbMATH Open document ID: 1058.62034
DOI: 10.1016/S0377-0427(02)00398-9
author: Anestis Antoniadis
author: Umberto Amato
author: Italia De Feis
published in: Journal of Computational and Applied Mathematics
publication date: 2003-02-23T00:00:00Z
review text: The authors consider Fourier series estimators of additive nonparametric models. They describe a Fourier direct separation method to build an estimator of the regression curve, both for uniform and random data. The convergence and asymptotic optimality of this method is proved. Many numerical tests are presented. The computational efficiency of this procedure is compared with other known methods.
reviewed by: Manfred Tasche
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62G20
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 65T40
Mathematics Subject Classification ID: 42B05
zbMATH DE Number: 1872838
zbMATH Keywords: Fourier series estimator
zbMATH Keywords: additive model
zbMATH Keywords: smoothing data
zbMATH Keywords: Fourier direct separation
zbMATH Keywords: uniform data
zbMATH Keywords: random data
zbMATH Keywords: regression curve
describes a project that uses: S-PLUS
describes a project that uses: FUNFITS
describes a project that uses: R
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1872838