Robust Estimators for Data Reconciliation

Autores
Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, a comparative performance analysis of robust data reconciliation strategies is presented. The study involves two procedures based on the biweight function and three estimation techniques that use the Welsh, quasi-weighted least squares, and correntropy M-estimators. The aforementioned functions are selected for comparative purposes because their use in the data reconciliation literature has appeared during the past decade. All procedures are properly tuned to have the same estimation and gross error detection/identification capabilities under the ideal distribution. Different measurement models are systematically taken into account, and results are analyzed considering both performance measures (average number of type I errors, global performance, mean square error) and computational load. The comparative analysis indicates that a simple robust methodology can provide a good balance between those two issues for linear and nonlinear benchmarks.
Fil: Llanos, Claudia Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Maronna, Ricardo Antonio. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Matematicas; Argentina
Materia
Data Reconciliation
Robust Statistics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/23707
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Robust Estimators for Data ReconciliationLlanos, Claudia ElizabethSanchez, Mabel CristinaMaronna, Ricardo AntonioData ReconciliationRobust Statisticshttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2In this work, a comparative performance analysis of robust data reconciliation strategies is presented. The study involves two procedures based on the biweight function and three estimation techniques that use the Welsh, quasi-weighted least squares, and correntropy M-estimators. The aforementioned functions are selected for comparative purposes because their use in the data reconciliation literature has appeared during the past decade. All procedures are properly tuned to have the same estimation and gross error detection/identification capabilities under the ideal distribution. Different measurement models are systematically taken into account, and results are analyzed considering both performance measures (average number of type I errors, global performance, mean square error) and computational load. The comparative analysis indicates that a simple robust methodology can provide a good balance between those two issues for linear and nonlinear benchmarks.Fil: Llanos, Claudia Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Maronna, Ricardo Antonio. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Matematicas; ArgentinaAmerican Chemical Society2015-04-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/23707Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; Robust Estimators for Data Reconciliation; American Chemical Society; Industrial & Engineering Chemical Research; 54; 18; 7-4-2015; 5096-51050888-5885CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1021/ie504735ainfo:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie504735ainfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:07:04Zoai:ri.conicet.gov.ar:11336/23707instacron: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:34982025-09-10 13:07:05.066CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust Estimators for Data Reconciliation
title Robust Estimators for Data Reconciliation
spellingShingle Robust Estimators for Data Reconciliation
Llanos, Claudia Elizabeth
Data Reconciliation
Robust Statistics
title_short Robust Estimators for Data Reconciliation
title_full Robust Estimators for Data Reconciliation
title_fullStr Robust Estimators for Data Reconciliation
title_full_unstemmed Robust Estimators for Data Reconciliation
title_sort Robust Estimators for Data Reconciliation
dc.creator.none.fl_str_mv Llanos, Claudia Elizabeth
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author Llanos, Claudia Elizabeth
author_facet Llanos, Claudia Elizabeth
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author_role author
author2 Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author2_role author
author
dc.subject.none.fl_str_mv Data Reconciliation
Robust Statistics
topic Data Reconciliation
Robust Statistics
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work, a comparative performance analysis of robust data reconciliation strategies is presented. The study involves two procedures based on the biweight function and three estimation techniques that use the Welsh, quasi-weighted least squares, and correntropy M-estimators. The aforementioned functions are selected for comparative purposes because their use in the data reconciliation literature has appeared during the past decade. All procedures are properly tuned to have the same estimation and gross error detection/identification capabilities under the ideal distribution. Different measurement models are systematically taken into account, and results are analyzed considering both performance measures (average number of type I errors, global performance, mean square error) and computational load. The comparative analysis indicates that a simple robust methodology can provide a good balance between those two issues for linear and nonlinear benchmarks.
Fil: Llanos, Claudia Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Maronna, Ricardo Antonio. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Matematicas; Argentina
description In this work, a comparative performance analysis of robust data reconciliation strategies is presented. The study involves two procedures based on the biweight function and three estimation techniques that use the Welsh, quasi-weighted least squares, and correntropy M-estimators. The aforementioned functions are selected for comparative purposes because their use in the data reconciliation literature has appeared during the past decade. All procedures are properly tuned to have the same estimation and gross error detection/identification capabilities under the ideal distribution. Different measurement models are systematically taken into account, and results are analyzed considering both performance measures (average number of type I errors, global performance, mean square error) and computational load. The comparative analysis indicates that a simple robust methodology can provide a good balance between those two issues for linear and nonlinear benchmarks.
publishDate 2015
dc.date.none.fl_str_mv 2015-04-07
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/11336/23707
Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; Robust Estimators for Data Reconciliation; American Chemical Society; Industrial & Engineering Chemical Research; 54; 18; 7-4-2015; 5096-5105
0888-5885
CONICET Digital
CONICET
url http://hdl.handle.net/11336/23707
identifier_str_mv Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; Robust Estimators for Data Reconciliation; American Chemical Society; Industrial & Engineering Chemical Research; 54; 18; 7-4-2015; 5096-5105
0888-5885
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1021/ie504735a
info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie504735a
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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 12.993085

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