Uncoupled isotonic regression via minimum Wasserstein deconvolution

DOI10.1093/imaiai/iaz006zbMath1471.62335arXiv1806.10648OpenAlexW2963609603WikidataQ128136478 ScholiaQ128136478MaRDI QIDQ5006508

Jonathan Weed, Philippe Rigollet

Publication date: 16 August 2021

Published in: Information and Inference: A Journal of the IMA (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.10648

zbMATH Keywords

deconvolutioncouplingisotonic regressionmoment matchingminimum Kantorovich distance estimation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) General nonlinear regression (62J02)

Related Items (14)

Isotonic regression with unknown permutations: statistics, computation and adaptation ⋮ Multivariate ranks and quantiles using optimal transport: consistency, rates and nonparametric testing ⋮ Minimax estimation of smooth densities in Wasserstein distance ⋮ A Pseudo-Likelihood Approach to Linear Regression With Partially Shuffled Data ⋮ Optimal Permutation Recovery in Permuted Monotone Matrix Model ⋮ Estimation of Wasserstein distances in the spiked transport model ⋮ Subgraph distributions in dense random regular graphs ⋮ Recovering Latent Variables by Matching ⋮ An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs ⋮ Worst-case versus average-case design for estimation from partial pairwise comparisons ⋮ Minimax estimation of smooth optimal transport maps ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Uncoupled Isotonic Regression with Discrete Errors

This page was built for publication: Uncoupled isotonic regression via minimum Wasserstein deconvolution