Density estimation of multivariate samples using Wasserstein distance

DOI10.1080/00949655.2019.1675661OpenAlexW2979368289MaRDI QIDQ5107701

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Publication date: 28 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11573/1344363

zbMATH Keywords

Wasserstein distancenonparametric density estimationmultivariate histogrampiecewise constant distribution

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Multiresolution approximation and consistent estimation of a multivariate density function ⋮ Dependence structure estimation using copula recursive trees

Uses Software

• ElemStatLearn
• EMD
• StInt

Cites Work

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• Density estimation with distribution element trees
• Limit laws of the empirical Wasserstein distance: Gaussian distributions
• A sparse multiscale algorithm for dense optimal transport
• Geodesic PCA in the Wasserstein space by convex PCA
• On optimal matchings
• Some asymptotic theory for the bootstrap
• Matching random samples in many dimensions
• Asymptotics for transportation cost in high dimensions
• A fast and objective multidimensional kernel density estimation method: fastKDE
• 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
• A distribution-free theory of nonparametric regression
• On a Formula for the L2 Wasserstein Metric between Measures on Euclidean and Hilbert Spaces
• Assignment Problems
• Smoothing of Multivariate Data
• Testing multivariate uniformity and its applications
• Statistical Intervals
• Inference for Empirical Wasserstein Distances on Finite Spaces
• Estimation of the information by an adaptive partitioning of the observation space
• Piecewise linear approximation of empirical distributions under a Wasserstein distance constraint
• On the Computation of Kantorovich--Wasserstein Distances Between Two-Dimensional Histograms by Uncapacitated Minimum Cost Flows
• Testing multivariate uniformity: The distance‐to‐boundary method
• A Note on Asymptotic Joint Normality
• Optimal Transport
• The elements of statistical learning. Data mining, inference, and prediction