Piecewise linear approximation of empirical distributions under a Wasserstein distance constraint

DOI10.1080/00949655.2018.1506454OpenAlexW2886787934WikidataQ129422549 ScholiaQ129422549MaRDI QIDQ4960757

William Guevara-Alarcón, Philipp Arbenz

Publication date: 23 April 2020

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

Full work available at URL: https://serval.unil.ch/notice/serval:BIB_0B54D99F9EE7

zbMATH Keywords

Monte Carlo simulationcompressionempirical distributionWasserstein distancepiecewise linear approximation

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Density estimation of multivariate samples using Wasserstein distance

Uses Software

• CASdatasets

Cites Work

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• Risk measure preserving piecewise linear approximation of empirical distributions
• Comparing distributions
• Central limit theorems for the Wasserstein distance between the empirical and the true distributions
• Foundations of quantization for probability distributions
• On Choosing and Bounding Probability Metrics
• One-dimensional empirical measures, order statistics, and Kantorovich transport distances
• Efficient density estimation via piecewise polynomial approximation
• Optimal Transport

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