Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure

Autores
Leiva, Ricardo Anibal; Roy, Anuradha
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this article, we study a new linear discriminant function for three-level m-variate observations under the assumption of multivariate normality. We assume that the m-variate observations have a doubly exchangeable covariance structure consisting of three unstructured covariance matrices for three multivariate levels and a separable additive structure on the mean vector. The new discriminant function is very efficient in discriminating individuals in a small sample scenario. An iterative algorithm is proposed to calculate the maximum likelihood estimates of the unknown population parameters as closed form solutions do not exist for these unknown parameters. The new discriminant function is applied to a real data set as well as to simulated data sets. We compare our findings with other linear discriminant functions for three-level multivariate data as well as with the traditional linear discriminant function.
Fil: Leiva, Ricardo Anibal. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Roy, Anuradha. University of Texas; Estados Unidos
Materia
ADDITIVE MEAN STRUCTURE
DOUBLY EXCHANGEABLE COVARIANCE STRUCTURE
LINEAR DISCRIMINANT FUNCTION
MAXIMUM LIKELIHOOD ESTIMATES
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:11336/198694
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/198694
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structureLeiva, Ricardo AnibalRoy, AnuradhaADDITIVE MEAN STRUCTUREDOUBLY EXCHANGEABLE COVARIANCE STRUCTURELINEAR DISCRIMINANT FUNCTIONMAXIMUM LIKELIHOOD ESTIMATEShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this article, we study a new linear discriminant function for three-level m-variate observations under the assumption of multivariate normality. We assume that the m-variate observations have a doubly exchangeable covariance structure consisting of three unstructured covariance matrices for three multivariate levels and a separable additive structure on the mean vector. The new discriminant function is very efficient in discriminating individuals in a small sample scenario. An iterative algorithm is proposed to calculate the maximum likelihood estimates of the unknown population parameters as closed form solutions do not exist for these unknown parameters. The new discriminant function is applied to a real data set as well as to simulated data sets. We compare our findings with other linear discriminant functions for three-level multivariate data as well as with the traditional linear discriminant function.Fil: Leiva, Ricardo Anibal. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Roy, Anuradha. University of Texas; Estados UnidosElsevier Science2012-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/198694Leiva, Ricardo Anibal; Roy, Anuradha; Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure; Elsevier Science; Computational Statistics and Data Analysis; 56; 6; 6-2012; 1644-16610167-9473CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167947311003641info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2011.10.007info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:36:35Zoai:ri.conicet.gov.ar:11336/198694instacron: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:34982025-10-15 14:36:35.667CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure
title Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure
spellingShingle Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure
Leiva, Ricardo Anibal
ADDITIVE MEAN STRUCTURE
DOUBLY EXCHANGEABLE COVARIANCE STRUCTURE
LINEAR DISCRIMINANT FUNCTION
MAXIMUM LIKELIHOOD ESTIMATES
title_short Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure
title_full Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure
title_fullStr Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure
title_full_unstemmed Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure
title_sort Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure
dc.creator.none.fl_str_mv Leiva, Ricardo Anibal
Roy, Anuradha
author Leiva, Ricardo Anibal
author_facet Leiva, Ricardo Anibal
Roy, Anuradha
author_role author
author2 Roy, Anuradha
author2_role author
dc.subject.none.fl_str_mv ADDITIVE MEAN STRUCTURE
DOUBLY EXCHANGEABLE COVARIANCE STRUCTURE
LINEAR DISCRIMINANT FUNCTION
MAXIMUM LIKELIHOOD ESTIMATES
topic ADDITIVE MEAN STRUCTURE
DOUBLY EXCHANGEABLE COVARIANCE STRUCTURE
LINEAR DISCRIMINANT FUNCTION
MAXIMUM LIKELIHOOD ESTIMATES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this article, we study a new linear discriminant function for three-level m-variate observations under the assumption of multivariate normality. We assume that the m-variate observations have a doubly exchangeable covariance structure consisting of three unstructured covariance matrices for three multivariate levels and a separable additive structure on the mean vector. The new discriminant function is very efficient in discriminating individuals in a small sample scenario. An iterative algorithm is proposed to calculate the maximum likelihood estimates of the unknown population parameters as closed form solutions do not exist for these unknown parameters. The new discriminant function is applied to a real data set as well as to simulated data sets. We compare our findings with other linear discriminant functions for three-level multivariate data as well as with the traditional linear discriminant function.
Fil: Leiva, Ricardo Anibal. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Roy, Anuradha. University of Texas; Estados Unidos
description In this article, we study a new linear discriminant function for three-level m-variate observations under the assumption of multivariate normality. We assume that the m-variate observations have a doubly exchangeable covariance structure consisting of three unstructured covariance matrices for three multivariate levels and a separable additive structure on the mean vector. The new discriminant function is very efficient in discriminating individuals in a small sample scenario. An iterative algorithm is proposed to calculate the maximum likelihood estimates of the unknown population parameters as closed form solutions do not exist for these unknown parameters. The new discriminant function is applied to a real data set as well as to simulated data sets. We compare our findings with other linear discriminant functions for three-level multivariate data as well as with the traditional linear discriminant function.
publishDate 2012
dc.date.none.fl_str_mv 2012-06
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/11336/198694
Leiva, Ricardo Anibal; Roy, Anuradha; Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure; Elsevier Science; Computational Statistics and Data Analysis; 56; 6; 6-2012; 1644-1661
0167-9473
CONICET Digital
CONICET
url http://hdl.handle.net/11336/198694
identifier_str_mv Leiva, Ricardo Anibal; Roy, Anuradha; Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure; Elsevier Science; Computational Statistics and Data Analysis; 56; 6; 6-2012; 1644-1661
0167-9473
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/S0167947311003641
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2011.10.007
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
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 13.22299

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