@article {
author = {Al-Qudaimi, Abdullah},
title = {A Parameterized Approach for Linear Regression of Interval Data: Suggested Modifications},
journal = {Fuzzy Optimization and Modeling Journal},
volume = {1},
number = {2},
pages = {60-68},
year = {2020},
publisher = {Islamic Azad University, Qaemshahr Branch},
issn = {2676-7007},
eissn = {2676-7007},
doi = {},
abstract = {Souza et al. (Knowledge-Based Systems, 131 (2017), pp. 149-159) pointed out that although several approaches have been proposed in the literature for fitting interval linear regression models (linear regression models its parameters are represented as intervals). However, as there are flaws in all the existing approaches, it is scientifically incorrect to use these approaches in real life problems. To resolve the flaws of the existing approaches, Souza et al. proposed a new approach for fitting interval linear regression models. After a deep study, it is observed that in the approach, proposed by Souza et al., some mathematical incorrect assumptions have been considered and hence, it is scientifically incorrect to use the Souza et al.’s approach, in real life problems. In this paper the mathematical incorrect assumption, considered by Souza et al, is pointed out and suggested modifications are provided as well as a new approach is proposed as for fitting the interval linear regression models. The proposed model guarantee mathematical coherent such that the predicted values of the model are intervals with lower bound less than or equal upper bound. Furthermore, the proposed has been illustrated with the help of a numerical example.},
keywords = {Interval linear regression fuzzy,Symbolic data analysis,Interval parameterization},
url = {http://fomj.qaemiau.ac.ir/article_678788.html},
eprint = {http://fomj.qaemiau.ac.ir/article_678788_0db809cd35f96ed5de8b2853c70b46dd.pdf}
}