Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution

Autores
Cantet, Rodolfo Juan Carlos; Birchmeier, Ana Nélida; Steibel, Juan Pedro
Año de publicación
2004
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A Markov chain Monte Carlo (MCMC) algorithm to sample an exchangeable covariance matrix, such as the one of the error terms (R0) in a multiple trait animal model withmissing records under normal-inverted Wishart priors is presented. The algorithm (FCG) isbased on a conjugate form of the inverted Wishart density that avoids sampling the missingerror terms. Normal prior densities are assumed for the ‘fixed’ effects and breeding values,whereas the covariance matrices are assumed to follow inverted Wishart distributions. The inverted Wishart prior for the environmental covariance matrix is a product density of all patternsof missing data. The resulting MCMC scheme eliminates the correlation between the sampledmissing residuals and the sampled R0, which in turn has the effect of decreasing the total amountof samples needed to reach convergence. The use of the FCG algorithm in a multiple trait dataset with an extreme pattern of missing records produced a dramatic reduction in the size of theautocorrelations among samples for all lags from 1 to 50, and this increased the effective samplesize from 2.5 to 7 times and reduced the number of samples needed to attain convergence, whencompared with the ‘data augmentation’ algorithm.
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Birchmeier, Ana Nélida. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina
Fil: Steibel, Juan Pedro. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina
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:11336/150860
Acceder
id CONICETDig_50d0ec59b956403fd4869f8ae7224bc6
oai_identifier_str oai:ri.conicet.gov.ar:11336/150860
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distributionCantet, Rodolfo Juan CarlosBirchmeier, Ana NélidaSteibel, Juan Pedrohttps://purl.org/becyt/ford/4.3https://purl.org/becyt/ford/4A Markov chain Monte Carlo (MCMC) algorithm to sample an exchangeable covariance matrix, such as the one of the error terms (R0) in a multiple trait animal model withmissing records under normal-inverted Wishart priors is presented. The algorithm (FCG) isbased on a conjugate form of the inverted Wishart density that avoids sampling the missingerror terms. Normal prior densities are assumed for the ‘fixed’ effects and breeding values,whereas the covariance matrices are assumed to follow inverted Wishart distributions. The inverted Wishart prior for the environmental covariance matrix is a product density of all patternsof missing data. The resulting MCMC scheme eliminates the correlation between the sampledmissing residuals and the sampled R0, which in turn has the effect of decreasing the total amountof samples needed to reach convergence. The use of the FCG algorithm in a multiple trait dataset with an extreme pattern of missing records produced a dramatic reduction in the size of theautocorrelations among samples for all lags from 1 to 50, and this increased the effective samplesize from 2.5 to 7 times and reduced the number of samples needed to attain convergence, whencompared with the ‘data augmentation’ algorithm.Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Birchmeier, Ana Nélida. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; ArgentinaFil: Steibel, Juan Pedro. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; ArgentinaBioMed Central2004-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/150860Cantet, Rodolfo Juan Carlos; Birchmeier, Ana Nélida; Steibel, Juan Pedro; Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution; BioMed Central; Genetics Selection Evolution; 36; 1; 12-2004; 49-640999-193X1297-9686CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://gsejournal.biomedcentral.com/articles/10.1186/1297-9686-36-1-49info:eu-repo/semantics/altIdentifier/doi/10.1186/1297-9686-36-1-49info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:05:48Zoai:ri.conicet.gov.ar:11336/150860instacron: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 15:05:48.458CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution
title Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution
spellingShingle Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution
Cantet, Rodolfo Juan Carlos
title_short Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution
title_full Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution
title_fullStr Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution
title_full_unstemmed Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution
title_sort Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution
dc.creator.none.fl_str_mv Cantet, Rodolfo Juan Carlos
Birchmeier, Ana Nélida
Steibel, Juan Pedro
author Cantet, Rodolfo Juan Carlos
author_facet Cantet, Rodolfo Juan Carlos
Birchmeier, Ana Nélida
Steibel, Juan Pedro
author_role author
author2 Birchmeier, Ana Nélida
Steibel, Juan Pedro
author2_role author
author
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.3
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv A Markov chain Monte Carlo (MCMC) algorithm to sample an exchangeable covariance matrix, such as the one of the error terms (R0) in a multiple trait animal model withmissing records under normal-inverted Wishart priors is presented. The algorithm (FCG) isbased on a conjugate form of the inverted Wishart density that avoids sampling the missingerror terms. Normal prior densities are assumed for the ‘fixed’ effects and breeding values,whereas the covariance matrices are assumed to follow inverted Wishart distributions. The inverted Wishart prior for the environmental covariance matrix is a product density of all patternsof missing data. The resulting MCMC scheme eliminates the correlation between the sampledmissing residuals and the sampled R0, which in turn has the effect of decreasing the total amountof samples needed to reach convergence. The use of the FCG algorithm in a multiple trait dataset with an extreme pattern of missing records produced a dramatic reduction in the size of theautocorrelations among samples for all lags from 1 to 50, and this increased the effective samplesize from 2.5 to 7 times and reduced the number of samples needed to attain convergence, whencompared with the ‘data augmentation’ algorithm.
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Birchmeier, Ana Nélida. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina
Fil: Steibel, Juan Pedro. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina
description A Markov chain Monte Carlo (MCMC) algorithm to sample an exchangeable covariance matrix, such as the one of the error terms (R0) in a multiple trait animal model withmissing records under normal-inverted Wishart priors is presented. The algorithm (FCG) isbased on a conjugate form of the inverted Wishart density that avoids sampling the missingerror terms. Normal prior densities are assumed for the ‘fixed’ effects and breeding values,whereas the covariance matrices are assumed to follow inverted Wishart distributions. The inverted Wishart prior for the environmental covariance matrix is a product density of all patternsof missing data. The resulting MCMC scheme eliminates the correlation between the sampledmissing residuals and the sampled R0, which in turn has the effect of decreasing the total amountof samples needed to reach convergence. The use of the FCG algorithm in a multiple trait dataset with an extreme pattern of missing records produced a dramatic reduction in the size of theautocorrelations among samples for all lags from 1 to 50, and this increased the effective samplesize from 2.5 to 7 times and reduced the number of samples needed to attain convergence, whencompared with the ‘data augmentation’ algorithm.
publishDate 2004
dc.date.none.fl_str_mv 2004-12
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/11336/150860
Cantet, Rodolfo Juan Carlos; Birchmeier, Ana Nélida; Steibel, Juan Pedro; Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution; BioMed Central; Genetics Selection Evolution; 36; 1; 12-2004; 49-64
0999-193X
1297-9686
CONICET Digital
CONICET
url http://hdl.handle.net/11336/150860
identifier_str_mv Cantet, Rodolfo Juan Carlos; Birchmeier, Ana Nélida; Steibel, Juan Pedro; Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution; BioMed Central; Genetics Selection Evolution; 36; 1; 12-2004; 49-64
0999-193X
1297-9686
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://gsejournal.biomedcentral.com/articles/10.1186/1297-9686-36-1-49
info:eu-repo/semantics/altIdentifier/doi/10.1186/1297-9686-36-1-49
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 BioMed Central
publisher.none.fl_str_mv BioMed Central
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
_version_ 1846083200626982912
score 13.22299

Publicaciones similares