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. (C) 2000 Elsevier Science B.V. All rights reserved.
A least-squares approach to fuzzy linear regression analysis / D'Urso, Pierpaolo; Tommaso, Gastaldi. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - 34:4(2000), pp. 427-440. [10.1016/s0167-9473(99)00109-7]
A least-squares approach to fuzzy linear regression analysis
D'URSO, Pierpaolo;
2000
Abstract
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. (C) 2000 Elsevier Science B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
