Upper and lower approximation models in interval regression using regression quantile techniques

DOI10.1016/S0377-2217(98)00191-XzbMath1180.62107OpenAlexW2093115432MaRDI QIDQ1610187

Hideo Tanaka, Haekwan Lee

Publication date: 18 August 2002

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0377-2217(98)00191-x

zbMATH Keywords

Linear programmingRegression quantileInterval regressionTrapezoidal fuzzy modelUpper and lower approximation models

Mathematics Subject Classification ID

Linear inference, regression (62J99) Applications of mathematical programming (90C90) Special problems of linear programming (transportation, multi-index, data envelopment analysis, etc.) (90C08) Fuzziness, and linear inference and regression (62J86)

Related Items

Interval regression by tolerance analysis approach ⋮ New methods of probabilistic and possibilistic interactive data processing ⋮ On the possibilistic approach to linear regression models involving uncertain, indeterminate or interval data ⋮ An understandable way to discover methods to model interval input-output samples ⋮ A minmax regret linear regression model under uncertainty in the dependent variable ⋮ A simple method for computation of fuzzy linear regression ⋮ Possibilistic linear regression with fuzzy data: tolerance approach with prior information ⋮ Dual models for possibilistic regression analysis ⋮ An integrated operation module for individual risk management ⋮ Quantile and expectile smoothing based on \(L_1\)-norm and \(L_2\)-norm fuzzy transforms

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