Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure

Autores
Allegrini, Franco; Braga, Jez W. B.; Moreira, Alessandro C. O.; Olivieri, Alejandro Cesar
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS).
Fil: Allegrini, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Braga, Jez W. B.. Universidade do Brasília; Brasil
Fil: Moreira, Alessandro C. O.. Universidade do Brasília; Brasil
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Materia
ERROR COVARIANCE MATRIX
MULTIVARIATE CALIBRATION
PENALIZED REGRESSION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/88366
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structureAllegrini, FrancoBraga, Jez W. B.Moreira, Alessandro C. O.Olivieri, Alejandro CesarERROR COVARIANCE MATRIXMULTIVARIATE CALIBRATIONPENALIZED REGRESSIONhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS).Fil: Allegrini, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaFil: Braga, Jez W. B.. Universidade do Brasília; BrasilFil: Moreira, Alessandro C. O.. Universidade do Brasília; BrasilFil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaElsevier Science2018-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/88366Allegrini, Franco; Braga, Jez W. B.; Moreira, Alessandro C. O.; Olivieri, Alejandro Cesar; Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure; Elsevier Science; Analytica Chimica Acta; 1011; 6-2018; 20-270003-2670CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0003267018301739info:eu-repo/semantics/altIdentifier/doi/10.1016/j.aca.2018.02.002info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:09:36Zoai:ri.conicet.gov.ar:11336/88366instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:09:36.545CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
title Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
spellingShingle Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
Allegrini, Franco
ERROR COVARIANCE MATRIX
MULTIVARIATE CALIBRATION
PENALIZED REGRESSION
title_short Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
title_full Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
title_fullStr Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
title_full_unstemmed Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
title_sort Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
dc.creator.none.fl_str_mv Allegrini, Franco
Braga, Jez W. B.
Moreira, Alessandro C. O.
Olivieri, Alejandro Cesar
author Allegrini, Franco
author_facet Allegrini, Franco
Braga, Jez W. B.
Moreira, Alessandro C. O.
Olivieri, Alejandro Cesar
author_role author
author2 Braga, Jez W. B.
Moreira, Alessandro C. O.
Olivieri, Alejandro Cesar
author2_role author
author
author
dc.subject.none.fl_str_mv ERROR COVARIANCE MATRIX
MULTIVARIATE CALIBRATION
PENALIZED REGRESSION
topic ERROR COVARIANCE MATRIX
MULTIVARIATE CALIBRATION
PENALIZED REGRESSION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS).
Fil: Allegrini, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Braga, Jez W. B.. Universidade do Brasília; Brasil
Fil: Moreira, Alessandro C. O.. Universidade do Brasília; Brasil
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
description A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS).
publishDate 2018
dc.date.none.fl_str_mv 2018-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/88366
Allegrini, Franco; Braga, Jez W. B.; Moreira, Alessandro C. O.; Olivieri, Alejandro Cesar; Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure; Elsevier Science; Analytica Chimica Acta; 1011; 6-2018; 20-27
0003-2670
CONICET Digital
CONICET
url http://hdl.handle.net/11336/88366
identifier_str_mv Allegrini, Franco; Braga, Jez W. B.; Moreira, Alessandro C. O.; Olivieri, Alejandro Cesar; Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure; Elsevier Science; Analytica Chimica Acta; 1011; 6-2018; 20-27
0003-2670
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0003267018301739
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.aca.2018.02.002
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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