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dc.contributor.author
Cook, R. Dennis
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dc.contributor.author
Forzani, Liliana Maria
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dc.date.available
2022-09-19T20:23:02Z
dc.date.issued
2020-10
dc.identifier.citation
Cook, R. Dennis; Forzani, Liliana Maria; Envelopes: A new chapter in partial least squares regression; John Wiley & Sons Ltd; Journal of Chemometrics; 34; 10; 10-2020
dc.identifier.issn
0886-9383
dc.identifier.uri
http://hdl.handle.net/11336/169385
dc.description.abstract
Partial least squares (PLS) regression has been a very popular method for prediction. The method can in a natural way be connected to a statistical model, which now has been extended and further developed in terms of an envelope model. Concentrating on the univariate case, several estimators of the regression vector in this model are defined, including the ordinary PLS estimator, the maximum likelihood envelope estimator, and a recently proposed Bayes PLS estimator. These are compared with respect to prediction error by systematic simulations. The simulations indicate that Bayes PLS performs well compared with the other methods. The model for partial least squares is presented in 5 ways. Three estimators in the model are introduced and compared through simulations. The ordinary partial least‐squares estimator does well, but the newly introduced Bayes estimator does better in many respects.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
John Wiley & Sons Ltd
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
PARTIAL LEAST SQUARE
dc.subject
SUFFICIENT DIMENSION REDUCTION
dc.subject
BIG DATA
dc.subject.classification
Estadística y Probabilidad
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dc.subject.classification
Matemáticas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Envelopes: A new chapter in partial least squares regression
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-03-14T21:07:37Z
dc.journal.volume
34
dc.journal.number
10
dc.journal.pais
Reino Unido
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dc.description.fil
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos
dc.description.fil
Fil: Forzani, Liliana Maria. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Departamento de Matemáticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Journal of Chemometrics
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1002/cem.3294
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/cem.3294
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