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dc.contributor.author
Allegrini, Franco
dc.contributor.author
Olivieri, Alejandro Cesar
dc.date.available
2018-07-23T17:45:11Z
dc.date.issued
2016-11
dc.identifier.citation
Allegrini, Franco; Olivieri, Alejandro Cesar; Multi-way figures of merit in the presence of heteroscedastic and correlated instrumental noise: Unfolded partial least-squares with residual multi-linearization; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 158; 11-2016; 200-209
dc.identifier.issn
0169-7439
dc.identifier.uri
http://hdl.handle.net/11336/52847
dc.description.abstract
In the presence of correlated and/or heteroscedastic noise, i.e., for measurement noise which is not independent and identically distributed (iid), new expressions are required to estimate multi-way calibration figures of merit. They are derived in the present report, with focus towards a useful multi-way approach based on unfolded partial least-squares with residual multi-linearization. The expressions allow one to estimate figures of merit under a generalized noise propagation scenario, and to gain insight into the various uncertainty sources contributing to the overall prediction error and limit of detection. Through the study of both simulated and experimental data, it is shown that significant differences exist between the values estimated assuming an iid noise structure and when the underlying structure deviates from this classical paradigm.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Correlated Noise
dc.subject
Figures of Merit
dc.subject
Heteroscedastic Noise
dc.subject
Residual Multi-Linearization
dc.subject
Unfolded Partial Least-Squares
dc.subject.classification
Otras Ciencias Químicas
dc.subject.classification
Ciencias Químicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Multi-way figures of merit in the presence of heteroscedastic and correlated instrumental noise: Unfolded partial least-squares with residual multi-linearization
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-07-23T12:56:21Z
dc.journal.volume
158
dc.journal.pagination
200-209
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
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
dc.description.fil
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
dc.journal.title
Chemometrics and Intelligent Laboratory Systems
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1016/j.chemolab.2016.09.001
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S016974391630288X
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