On minimax convergence rates under \(L^p\)-risk for the anisotropic functional deconvolution model

DOI10.1016/j.spl.2017.07.008zbMath1391.62054OpenAlexW2739601987MaRDI QIDQ2407534

Rida Benhaddou

Publication date: 6 October 2017

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2017.07.008

zbMATH Keywords

Besov spaceminimax convergence rateanisotropic functional deconvolution

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items (3)

Minimax adaptive wavelet estimator for the anisotropic functional deconvolution model with unknown kernel ⋮ Anisotropic functional deconvolution for the irregular design: A minimax study ⋮ Anisotropic functional deconvolution with long-memory noise: the case of a multi-parameter fractional Wiener sheet

Cites Work

• Anisotropic de-noising in functional deconvolution model with dimension-free convergence rates
• On convergence rates equivalency and sampling strategies in functional deconvolution models
• Functional deconvolution in a periodic setting: uniform case
• Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition
• Multichannel boxcar deconvolution with growing number of channels
• Minimax convergence rates under the \(L^p\)-risk in the functional deconvolution model
• Aggregation for Gaussian regression
• Wavelet decomposition approaches to statistical inverse problems
• Wavelet Deconvolution in a Periodic Setting
• TRANSLATION INVARIANT DECONVOLUTION IN A PERIODIC SETTING
• Deconvolution using Meyer wavelets

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