The OWA operator in multiple linear regression

被引：19
作者：
Flores-Sosa M. [1 ]
Avilés-Ochoa E. [1 ]
Merigó J.M. [2 ,3 ]
Kacprzyk J. [4 ]
机构：
[1] University Autonomous of Occidente, Av. Lola Beltran, Culiacán
[2] School of Information, Systems & Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, 81 Broadway, Ultimo
[3] Department of Management Control and Information Systems, School of Economics and Business, University of Chile, Av. Diagonal 257, Santiago
[4] Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw
来源：
Applied Soft Computing | 2022年 / 124卷
关键词：
GOWA operator; Multiple linear regression; OLS; OWA operator;
D O I：
10.1016/j.asoc.2022.108985
中图分类号：
学科分类号：
摘要：
Multiple linear regression (MLR) is one of the most widely used statistical procedures for scholarly and research. The main limitation of MLR is that when being estimated with linear methodologies as ordinary least squares (OLS) becomes not functional with complex data. The ordered weighted average (OWA) is an aggregation operator that provides means that collect complex information. This work presents a new application that uses MLR and OWA operators in the same formulation. We developed two applications called MLR-OWA operator and MLR-GOWA operator. The main advantage of the MLR with OWA operators is that we can consider the degree of optimism and pessimism of the environment. We study some of its main properties and particular cases. Finally, an application is tested for a volatility exchange rate estimation problem. © 2022 Elsevier B.V.
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