Multiple Fourier series procedures for extraction of nonlinear regressions from noisy data

DOI10.1109/78.277809zbMath0785.62044OpenAlexW2162803824MaRDI QIDQ4276687

Leszek Rutkowski

Publication date: 13 February 1994

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/78.277809

zbMATH Keywords

noisy datamultiple Fourier seriesoptimal rate of convergencenumber of observationsmean integrated square errortwo-dimensional caseDirichlet kernelsFejer kernelsde la Vallée Poussin multiple kernelsorder of kernels

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) General nonlinear regression (62J02)

Related Items (2)

Parallel Approach to the Levenberg-Marquardt Learning Algorithm for Feedforward Neural Networks ⋮ Wavelets and distributed approximating functionals

This page was built for publication: Multiple Fourier series procedures for extraction of nonlinear regressions from noisy data