Adaptive circular deconvolution by model selection under unknown error distribution

DOI10.3150/12-BEJ422zbMath1280.62044arXiv0912.1207MaRDI QIDQ2435214

Maik Schwarz, Jan Johannes

Publication date: 4 February 2014

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0912.1207

zbMATH Keywords

model selectionadaptive density estimationminimax theoryspectral cut-offorthogonal series estimation

Mathematics Subject Classification ID

Related Items (17)

A note on a fixed-point method for deconvolution ⋮ Semiparametric inference for mixtures of circular data ⋮ Recent advances in directional statistics ⋮ Density estimation for circular data observed with errors ⋮ Concentration inequalities for Poisson point processes with application to adaptive intensity estimation ⋮ Deconvolution of spherical data corrupted with unknown noise ⋮ Laguerre deconvolution with unknown matrix operator ⋮ Adaptive nonparametric estimation for Lévy processes observed at low frequency ⋮ Blockwise SVD with error in the operator and application to blind deconvolution ⋮ Noisy Laplace deconvolution with error in the operator ⋮ Adaptive density estimation in deconvolution problems with unknown error distribution ⋮ Adaptive estimation of marginal random-effects densities in linear mixed-effects models ⋮ Adaptive minimax testing for circular convolution ⋮ Nonparametric adaptive estimation for grouped data ⋮ Adaptive quantile estimation in deconvolution with unknown error distribution ⋮ Nonparametric intensity estimation from noisy observations of a Poisson process under unknown error distribution ⋮ Density estimation for mixed Euclidean and non-Euclidean data in the presence of measurement error

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