Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics
Autor(a) principal: | |
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Data de Publicação: | 2024 |
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/10071/31170 |
Resumo: | In early work we have studied a class of stochastic differential equation (SDE) models, for which the Gompertz and the Bertalanffy-Richards stochastic models are particular cases, to describe individual growth in random environments, and applied it to model cattle weight evolution using real data. We have started to work on these type of models considering that the model parameters are fixed, i.e. the same for all the animals. Aiming to incorporate variability among individuals, we consider that the model parameters can be random variables, resulting in SDE mixed models. In additon, here we consider SDE mixed models, allowing the parameters to be random and propose to incorporate each animal's genetic characteristics considering the transformed animal's weight at maturity to be a function of its genetic values. The main objective is to extend the SDE mixed model to the more realistic case where the individual genetic value becomes an important component in the estimated growth curve. For the estimation of the model parameters we have used maximum likelihood estimation theory. Estimates and asymptotic confidence intervals of the parameters are presented. A comparison with SDE non-mixed model and SDE mixed model without the inclusion of genetic characteristics is shown with the conclusion that the incorporation of some genetic characteristics in the model parameters improves the estimation of the animal's growth curve. |
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Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristicsGenetic traitsIndividual growthMixed modelsStochastic differential equationsIn early work we have studied a class of stochastic differential equation (SDE) models, for which the Gompertz and the Bertalanffy-Richards stochastic models are particular cases, to describe individual growth in random environments, and applied it to model cattle weight evolution using real data. We have started to work on these type of models considering that the model parameters are fixed, i.e. the same for all the animals. Aiming to incorporate variability among individuals, we consider that the model parameters can be random variables, resulting in SDE mixed models. In additon, here we consider SDE mixed models, allowing the parameters to be random and propose to incorporate each animal's genetic characteristics considering the transformed animal's weight at maturity to be a function of its genetic values. The main objective is to extend the SDE mixed model to the more realistic case where the individual genetic value becomes an important component in the estimated growth curve. For the estimation of the model parameters we have used maximum likelihood estimation theory. Estimates and asymptotic confidence intervals of the parameters are presented. A comparison with SDE non-mixed model and SDE mixed model without the inclusion of genetic characteristics is shown with the conclusion that the incorporation of some genetic characteristics in the model parameters improves the estimation of the animal's growth curve.International Academic Press2024-02-23T12:11:55Z2024-01-01T00:00:00Z20242024-02-23T12:11:21Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/31170eng2311-004X10.19139/soic-2310-5070-1829Jamba, N. T.Filipe, P. A.Jacinto, G.Braumann, C. A.info: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:RCAAP2024-02-25T01:18:29Zoai:repositorio.iscte-iul.pt:10071/31170Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:11:19.791646Repositório 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 |
Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics |
title |
Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics |
spellingShingle |
Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics Jamba, N. T. Genetic traits Individual growth Mixed models Stochastic differential equations |
title_short |
Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics |
title_full |
Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics |
title_fullStr |
Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics |
title_full_unstemmed |
Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics |
title_sort |
Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics |
author |
Jamba, N. T. |
author_facet |
Jamba, N. T. Filipe, P. A. Jacinto, G. Braumann, C. A. |
author_role |
author |
author2 |
Filipe, P. A. Jacinto, G. Braumann, C. A. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Jamba, N. T. Filipe, P. A. Jacinto, G. Braumann, C. A. |
dc.subject.por.fl_str_mv |
Genetic traits Individual growth Mixed models Stochastic differential equations |
topic |
Genetic traits Individual growth Mixed models Stochastic differential equations |
description |
In early work we have studied a class of stochastic differential equation (SDE) models, for which the Gompertz and the Bertalanffy-Richards stochastic models are particular cases, to describe individual growth in random environments, and applied it to model cattle weight evolution using real data. We have started to work on these type of models considering that the model parameters are fixed, i.e. the same for all the animals. Aiming to incorporate variability among individuals, we consider that the model parameters can be random variables, resulting in SDE mixed models. In additon, here we consider SDE mixed models, allowing the parameters to be random and propose to incorporate each animal's genetic characteristics considering the transformed animal's weight at maturity to be a function of its genetic values. The main objective is to extend the SDE mixed model to the more realistic case where the individual genetic value becomes an important component in the estimated growth curve. For the estimation of the model parameters we have used maximum likelihood estimation theory. Estimates and asymptotic confidence intervals of the parameters are presented. A comparison with SDE non-mixed model and SDE mixed model without the inclusion of genetic characteristics is shown with the conclusion that the incorporation of some genetic characteristics in the model parameters improves the estimation of the animal's growth curve. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-02-23T12:11:55Z 2024-01-01T00:00:00Z 2024 2024-02-23T12:11:21Z |
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/10071/31170 |
url |
http://hdl.handle.net/10071/31170 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2311-004X 10.19139/soic-2310-5070-1829 |
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.publisher.none.fl_str_mv |
International Academic Press |
publisher.none.fl_str_mv |
International Academic Press |
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 |
repository.mail.fl_str_mv |
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1799137763384098816 |