Nonparametric regression estimation based on spatially inhomogeneous data: minimax global convergence rates and adaptivity

DOI10.1051/ps/2012024zbMath1305.62167arXiv1106.4461OpenAlexW2105405388MaRDI QIDQ5174342

Anestis Antoniadis, Theofanis Sapatinas, Marianna Pensky

Publication date: 17 February 2015

Published in: ESAIM: Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1106.4461

zbMATH Keywords

adaptivityBesov spacesthresholdingnonparametric regressionminimax estimationwavelet estimationinhomogeneous data

Mathematics Subject Classification ID

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

Related Items (5)

Anisotropic functional deconvolution for the irregular design: A minimax study ⋮ A robust, adaptive M-estimator for pointwise estimation in heteroscedastic regression ⋮ Spatially inhomogeneous linear inverse problems with possible singularities ⋮ A note on wavelet estimation of the derivatives of a regression function in a random design setting ⋮ Anisotropic functional deconvolution with long-memory noise: the case of a multi-parameter fractional Wiener sheet

This page was built for publication: Nonparametric regression estimation based on spatially inhomogeneous data: minimax global convergence rates and adaptivity