The OWA operator in multiple linear regression

Flores-Sosa, M; Avilés-Ochoa, E; Merigó, JM; Kacprzyk, J

The OWA operator in multiple linear regression

Flores-Sosa, M Avilés-Ochoa, E Merigó, JM Kacprzyk, J

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Applied Soft Computing, 2022, 124, pp. 1-11
Issue Date:: 2022-07-01

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Field	Value	Language
dc.contributor.author	Flores-Sosa, M
dc.contributor.author	Avilés-Ochoa, E
dc.contributor.author	Merigó, JM
dc.contributor.author	Kacprzyk, J
dc.date.accessioned	2023-03-21T23:28:25Z
dc.date.available	2023-03-21T23:28:25Z
dc.date.issued	2022-07-01
dc.identifier.citation	Applied Soft Computing, 2022, 124, pp. 1-11
dc.identifier.issn	1568-4946
dc.identifier.uri	http://hdl.handle.net/10453/168005
dc.description.abstract	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.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Applied Soft Computing
dc.relation.isbasedon	10.1016/j.asoc.2022.108985
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	The OWA operator in multiple linear regression
dc.type	Journal Article
utslib.citation.volume	124
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-21T23:28:24Z
pubs.publication-status	Published
pubs.volume	124

Abstract:

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.

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http://hdl.handle.net/10453/168005