Joining models with commutative orthogonal block structure
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.6/9372 |
Resumo: | Mixed linear models are a versatile and powerful tool for analysing data collected in experiments in several areas. Amixed model is a model with orthogonal block structure, OBS, when its variance–covariance matrix is ofall the positive semi-definite linear combinations of known pairwise orthogo-nal orthogonal projection matrices that add up to the identity matrix. Models with commutative orthogonal block structure, COBS, are a special case of OBS in which the orthogonal projection matrix on the space spanned by the mean vector commutes with the variance–covariance matrix. Using the algebraic structure of COBS, based on Commuta-tive Jordan algebras of symmetric matrices, and the Carte-sian product we build up complex models from simpler ones through joining, in order to analyse together models obtained independently. This commutativity condition of COBS is a necessary and sufficient condition for the least square esti-mators, LSE, to be best linear unbiased estimators, BLUE, whatever the variance components. Since joining COBS we obtain new COBS, the good properties of estimators hold for the joined models. |
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Joining models with commutative orthogonal block structureJordan algebraMixed modelsModels with commutative orthogonal block structureModels joiningMixed linear models are a versatile and powerful tool for analysing data collected in experiments in several areas. Amixed model is a model with orthogonal block structure, OBS, when its variance–covariance matrix is ofall the positive semi-definite linear combinations of known pairwise orthogo-nal orthogonal projection matrices that add up to the identity matrix. Models with commutative orthogonal block structure, COBS, are a special case of OBS in which the orthogonal projection matrix on the space spanned by the mean vector commutes with the variance–covariance matrix. Using the algebraic structure of COBS, based on Commuta-tive Jordan algebras of symmetric matrices, and the Carte-sian product we build up complex models from simpler ones through joining, in order to analyse together models obtained independently. This commutativity condition of COBS is a necessary and sufficient condition for the least square esti-mators, LSE, to be best linear unbiased estimators, BLUE, whatever the variance components. Since joining COBS we obtain new COBS, the good properties of estimators hold for the joined models.uBibliorumSantos, CarlaNunes, CéliaDias, CristinaMexia, João T.2020-02-19T14:46:57Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/9372eng10.1016/j.laa.2016.12.019metadata only accessinfo: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:RCAAP2023-12-15T09:50:24Zoai:ubibliorum.ubi.pt:10400.6/9372Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:49:31.642794Repositório 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 |
Joining models with commutative orthogonal block structure |
title |
Joining models with commutative orthogonal block structure |
spellingShingle |
Joining models with commutative orthogonal block structure Santos, Carla Jordan algebra Mixed models Models with commutative orthogonal block structure Models joining |
title_short |
Joining models with commutative orthogonal block structure |
title_full |
Joining models with commutative orthogonal block structure |
title_fullStr |
Joining models with commutative orthogonal block structure |
title_full_unstemmed |
Joining models with commutative orthogonal block structure |
title_sort |
Joining models with commutative orthogonal block structure |
author |
Santos, Carla |
author_facet |
Santos, Carla Nunes, Célia Dias, Cristina Mexia, João T. |
author_role |
author |
author2 |
Nunes, Célia Dias, Cristina Mexia, João T. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
uBibliorum |
dc.contributor.author.fl_str_mv |
Santos, Carla Nunes, Célia Dias, Cristina Mexia, João T. |
dc.subject.por.fl_str_mv |
Jordan algebra Mixed models Models with commutative orthogonal block structure Models joining |
topic |
Jordan algebra Mixed models Models with commutative orthogonal block structure Models joining |
description |
Mixed linear models are a versatile and powerful tool for analysing data collected in experiments in several areas. Amixed model is a model with orthogonal block structure, OBS, when its variance–covariance matrix is ofall the positive semi-definite linear combinations of known pairwise orthogo-nal orthogonal projection matrices that add up to the identity matrix. Models with commutative orthogonal block structure, COBS, are a special case of OBS in which the orthogonal projection matrix on the space spanned by the mean vector commutes with the variance–covariance matrix. Using the algebraic structure of COBS, based on Commuta-tive Jordan algebras of symmetric matrices, and the Carte-sian product we build up complex models from simpler ones through joining, in order to analyse together models obtained independently. This commutativity condition of COBS is a necessary and sufficient condition for the least square esti-mators, LSE, to be best linear unbiased estimators, BLUE, whatever the variance components. Since joining COBS we obtain new COBS, the good properties of estimators hold for the joined models. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2017-01-01T00:00:00Z 2020-02-19T14:46:57Z |
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/10400.6/9372 |
url |
http://hdl.handle.net/10400.6/9372 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.laa.2016.12.019 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
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 |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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1799136387091398656 |