Estimation and incommutativity in mixed models

Detalhes bibliográficos
Autor(a) principal: Ferreira, Dário
Data de Publicação: 2017
Outros Autores: Ferreira, Sandra S., Nunes, Célia, Fonseca, Miguel, Silva, Adilson, Mexia, João T.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.6/9069
Resumo: In this paper we present a treatment for the estimation of variance components and estimable vectors in linear mixed models in which the relation matrices may not commute. To overcome this difficulty, we partition the mixed model in sub-models using orthogonal matrices. In addition, we obtain confidence regions and derive tests of hypothesis for the variance components. A numerical example is included. There we illustrate the estimation of the variance components using our treatment and compare the obtained estimates with the ones obtained by the ANOVA method. Besides this, we also present the restricted and unrestricted maximum likelihood estimates.
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spelling Estimation and incommutativity in mixed modelsInferenceMixed modelsVariance componentsIn this paper we present a treatment for the estimation of variance components and estimable vectors in linear mixed models in which the relation matrices may not commute. To overcome this difficulty, we partition the mixed model in sub-models using orthogonal matrices. In addition, we obtain confidence regions and derive tests of hypothesis for the variance components. A numerical example is included. There we illustrate the estimation of the variance components using our treatment and compare the obtained estimates with the ones obtained by the ANOVA method. Besides this, we also present the restricted and unrestricted maximum likelihood estimates.uBibliorumFerreira, DárioFerreira, Sandra S.Nunes, CéliaFonseca, MiguelSilva, AdilsonMexia, João T.2020-02-06T15:35:46Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/9069eng10.1016/j.jmva.2017.07.002info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-15T09:49:42Zoai:ubibliorum.ubi.pt:10400.6/9069Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:49:18.539088Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Estimation and incommutativity in mixed models
title Estimation and incommutativity in mixed models
spellingShingle Estimation and incommutativity in mixed models
Ferreira, Dário
Inference
Mixed models
Variance components
title_short Estimation and incommutativity in mixed models
title_full Estimation and incommutativity in mixed models
title_fullStr Estimation and incommutativity in mixed models
title_full_unstemmed Estimation and incommutativity in mixed models
title_sort Estimation and incommutativity in mixed models
author Ferreira, Dário
author_facet Ferreira, Dário
Ferreira, Sandra S.
Nunes, Célia
Fonseca, Miguel
Silva, Adilson
Mexia, João T.
author_role author
author2 Ferreira, Sandra S.
Nunes, Célia
Fonseca, Miguel
Silva, Adilson
Mexia, João T.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv uBibliorum
dc.contributor.author.fl_str_mv Ferreira, Dário
Ferreira, Sandra S.
Nunes, Célia
Fonseca, Miguel
Silva, Adilson
Mexia, João T.
dc.subject.por.fl_str_mv Inference
Mixed models
Variance components
topic Inference
Mixed models
Variance components
description In this paper we present a treatment for the estimation of variance components and estimable vectors in linear mixed models in which the relation matrices may not commute. To overcome this difficulty, we partition the mixed model in sub-models using orthogonal matrices. In addition, we obtain confidence regions and derive tests of hypothesis for the variance components. A numerical example is included. There we illustrate the estimation of the variance components using our treatment and compare the obtained estimates with the ones obtained by the ANOVA method. Besides this, we also present the restricted and unrestricted maximum likelihood estimates.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01T00:00:00Z
2020-02-06T15:35:46Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.6/9069
url http://hdl.handle.net/10400.6/9069
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1016/j.jmva.2017.07.002
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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