Linear regression model with histogram‐valued variables

DOI10.1002/sam.11260OpenAlexW1499131690WikidataQ57675539 ScholiaQ57675539MaRDI QIDQ4969995

Paula Brito, Sónia Dias

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1303.6199

zbMATH Keywords

linear regressionMallows distancesymbolic data analysisquantile functionsdata with variability

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (10)

Dynamic quantile function models ⋮ New models for symbolic data analysis ⋮ Parametrized linear regression for boxplot-multivalued data applied to the Brazilian electric sector ⋮ M-LDQ feature embedding and regression modeling for distribution-valued data ⋮ Classification of histogram-valued data with support histogram machines ⋮ Composite likelihood methods for histogram-valued random variables ⋮ Spatial prediction and spatial dependence monitoring on georeferenced data streams ⋮ Off the beaten track: a new linear model for interval data ⋮ Discriminant analysis of distributional data via fractional programming ⋮ Artificial Neural Network with Histogram Data Time Series Forecasting: A Least Squares Approach Based on Wasserstein Distance

Uses Software

• SODAS

Cites Work

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• Basic statistics for distributional symbolic variables: a new metric-based approach
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• Interval Arithmetic and Analysis
• Ordinary Least Squares for Histogram Data Based on Wasserstein Distance
• Symbolic Data Analysis
• A Powerful Numerical Method to Combine Random Variables
• Far beyond the classical data models: symbolic data analysis
• Modelling interval data with Normal and Skew-Normal distributions
• Bivariate symbolic regression models for interval-valued variables
• Symbolic Data Analysis and the SODAS Software
• A Note on Asymptotic Joint Normality
• Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data

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