A least-squares approach to fuzzy linear regression analysis.
type: Item
instance of: scholarly article
title: A least-squares approach to fuzzy linear regression analysis.
zbMATH Open document ID: 1046.62066
DOI: 10.1016/S0167-9473(99)00109-7
published in: Computational Statistics and Data Analysis
publication date: 2000-10-26T00:00:00Z
review text: This paper deals with a new approach to fuzzy linear regression analysis. A doubly linear adaptive fuzzy regression model is proposed, based on two linear models: a core regression model and a spread regression model. The first one ``explains'' the centers of the fuzzy observations, while the second one is for their spreads. As dependence between centers and spreads is often encountered in real world applications, our model is defined in such a way as to take into account a possible linear relationship among centers and spreads. Illustrative examples are also discussed, and a computer program which implements our procedure is enclosed.
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62J99
Mathematics Subject Classification ID: 65C60
zbMATH DE Number: 1521873
zbMATH Keywords: Fuzzy data
zbMATH Keywords: doubly linear adaptive fuzzy regression model
zbMATH Keywords: least-squares estimators
author: Pierpaolo D'Urso
author: Tommaso Gastaldi
MaRDI profile type: Publication
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full work available at URL: s0167-9473(99)00109-7
OpenAlex ID: W2022345854
description: scientific article; zbMATH DE number 1521873