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dc.contributor.author
Cook, R. Dennis  
dc.contributor.author
Forzani, Liliana Maria  
dc.date.available
2022-08-10T15:43:01Z  
dc.date.issued
2021-06  
dc.identifier.citation
Cook, R. Dennis; Forzani, Liliana Maria; PLS regression algorithms in the presence of nonlinearity; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 213; 6-2021; 1-13  
dc.identifier.issn
0169-7439  
dc.identifier.uri
http://hdl.handle.net/11336/164987  
dc.description.abstract
It has long been emphasized that standard PLS regression algorithms like NIPALS and SIMPLS are not suitable for regressions in which there is a nonlinear relationship between the response and the predictors. We show that this conclusion, while strictly true, fails to recognize that aspects of these algorithms remain serviceable in the presence of nonlinearity. In particular, the dimension reduction step of these standard algorithms is serviceable under linear and nonlinear relationships, while the predictive step is not. Additionally, we propose graphical methods for diagnosing nonlinearity, develop a novel method of nonlinear prediction based on reduced predictors arising from standard PLS regression algorithms and demonstrate the effectiveness of our approach in two case studies.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier Science  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
CENTRAL MEAN SUBSPACE  
dc.subject
ENVELOPES  
dc.subject
GRAPHICAL DIAGNOSTICS  
dc.subject
KRYLOV SEQUENCES  
dc.subject
NIPALS  
dc.subject
SIMPLS  
dc.subject.classification
Estadística y Probabilidad  
dc.subject.classification
Matemáticas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
PLS regression algorithms in the presence of nonlinearity  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2022-08-08T15:14:11Z  
dc.journal.volume
213  
dc.journal.pagination
1-13  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos  
dc.description.fil
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Departamento de Matemáticas; Argentina  
dc.journal.title
Chemometrics and Intelligent Laboratory Systems  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0169743921000757  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.chemolab.2021.104307