Improved rates for Wasserstein deconvolution with ordinary smooth error in dimension one

DOI10.1214/15-EJS997zbMath1307.62092arXiv1404.0646OpenAlexW2963723039MaRDI QIDQ2259535

Bertrand Michel, Aurélie Fischer, Jérôme Dedecker

Publication date: 4 March 2015

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.0646

zbMATH Keywords

deconvolutionWasserstein metricsminimax rates

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Minimax procedures in statistical decision theory (62C20)

Related Items (3)

Unnamed Item ⋮ Bayes and maximum likelihood for \(L^1\)-Wasserstein deconvolution of Laplace mixtures ⋮ Minimax estimation of smooth optimal transport maps

Uses Software

• isotone

Cites Work

• Unnamed Item
• Constructive quantization: approximation by empirical measures
• Minimax rates of convergence for Wasserstein deconvolution with supersmooth errors in any dimension
• On the rate of convergence in Wasserstein distance of the empirical measure
• Geometric inference for probability measures
• On deconvolution of distribution functions
• Adaptively local one-dimensional subproblems with application to a deconvolution problem
• On the optimal rates of convergence for nonparametric deconvolution problems
• Deconvolution problems in nonparametric statistics
• Estimation of distributions, moments and quantiles in deconvolution problems
• Asymptotics for \(L_2\) functionals of the empirical quantile process, with applications to tests of fit based on weighted Wasserstein distances
• Central limit theorems for the Wasserstein distance between the empirical and the true distributions
• Bootstrap bandwidth selection in kernel density estimation from a contaminated sample
• Weak convergence and empirical processes. With applications to statistics
• Deconvolution for the Wasserstein metric and geometric inference
• On weighted isoperimetric and Poincaré-type inequalities
• Sharp Optimality in Density Deconvolution with Dominating Bias. I
• Sharp Optimality in Density Deconvolution with Dominating Bias. II
• Topology and data
• Optimal Rates of Convergence for Deconvolving a Density
• Inequalities for Probabilities of Large Deviations in Terms of Pseudo-Moments

This page was built for publication: Improved rates for Wasserstein deconvolution with ordinary smooth error in dimension one