Ordinary Least Squares for Histogram Data Based on Wasserstein Distance

DOI10.1007/978-3-7908-2604-3_60zbMath1436.62334OpenAlexW2277272179MaRDI QIDQ3298519

Antonio Irpino, Rosanna Verde

Publication date: 14 July 2020

Published in: Proceedings of COMPSTAT'2010 (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-7908-2604-3_60

zbMATH Keywords

probability distribution functionordinary least squaresWasserstein distancehistogram data

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05)

Related Items (5)

Linear regression for numeric symbolic variables: a least squares approach based on Wasserstein distance ⋮ Parametrized linear regression for boxplot-multivalued data applied to the Brazilian electric sector ⋮ Discriminant analysis of distributional data via fractional programming ⋮ Clustering of modal-valued symbolic data ⋮ Linear regression model with histogram‐valued variables

Cites Work

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• Optimal transportation plans and convergence in distribution
• Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator
• Symbolic Data Analysis
• On Choosing and Bounding Probability Metrics
• Dependencies and Variation Components of Symbolic Interval-Valued Data
• Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data

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