Multiphasic individual growth models in random environments
Autor(a) principal: | |
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Data de Publicação: | 2011 |
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/10174/2476 |
Resumo: | The evolution of the growth of an individual in a random environment can be described through stochastic differential equations of the form dY(t) = b(a-Y(t))dt+sdW(t), where Y(t)=h(X(t)), X(t) is the size of the individual at age t, h is a strictly increasing continuously differentiable function, a=h(A), where A is the average asymptotic size, and b represents the rate of approach to maturity. The parameter s measures the intensity of the effect of random fluctuations on growth and W(t) is the standard Wiener process. We have previously applied this monophasic model, in which there is only one functional form describing the average dynamics of the complete growth curve, and studied the estimation issues. Here, we present the generalization of the above stochastic model to the multiphasic case, in which we consider that the growth coefficient b assumes different values for different phases of the animal life. For simplicity, we consider two phases with growth coefficients b1 and b2. Results and methods are illustrated using bovine growth data. |
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Multiphasic individual growth models in random environmentsMulthiphasic growth modelsStochastic differential equationsEstimationCattle weightThe evolution of the growth of an individual in a random environment can be described through stochastic differential equations of the form dY(t) = b(a-Y(t))dt+sdW(t), where Y(t)=h(X(t)), X(t) is the size of the individual at age t, h is a strictly increasing continuously differentiable function, a=h(A), where A is the average asymptotic size, and b represents the rate of approach to maturity. The parameter s measures the intensity of the effect of random fluctuations on growth and W(t) is the standard Wiener process. We have previously applied this monophasic model, in which there is only one functional form describing the average dynamics of the complete growth curve, and studied the estimation issues. Here, we present the generalization of the above stochastic model to the multiphasic case, in which we consider that the growth coefficient b assumes different values for different phases of the animal life. For simplicity, we consider two phases with growth coefficients b1 and b2. Results and methods are illustrated using bovine growth data.2011-01-20T11:23:32Z2011-01-202011-01-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article154805 bytesapplication/pdfhttp://hdl.handle.net/10174/2476http://hdl.handle.net/10174/2476engMethodology and Computing in Applied Probabilitylivrepasf@uevora.ptbraumann@uevora.ptcroquete@uevora.pt336Filipe, Patrícia A.Braumann, Carlos A.Roquete, Carlos J.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:RCAAP2024-01-03T18:39:00Zoai:dspace.uevora.pt:10174/2476Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:58:11.586643Repositório 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 |
Multiphasic individual growth models in random environments |
title |
Multiphasic individual growth models in random environments |
spellingShingle |
Multiphasic individual growth models in random environments Filipe, Patrícia A. Multhiphasic growth models Stochastic differential equations Estimation Cattle weight |
title_short |
Multiphasic individual growth models in random environments |
title_full |
Multiphasic individual growth models in random environments |
title_fullStr |
Multiphasic individual growth models in random environments |
title_full_unstemmed |
Multiphasic individual growth models in random environments |
title_sort |
Multiphasic individual growth models in random environments |
author |
Filipe, Patrícia A. |
author_facet |
Filipe, Patrícia A. Braumann, Carlos A. Roquete, Carlos J. |
author_role |
author |
author2 |
Braumann, Carlos A. Roquete, Carlos J. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Filipe, Patrícia A. Braumann, Carlos A. Roquete, Carlos J. |
dc.subject.por.fl_str_mv |
Multhiphasic growth models Stochastic differential equations Estimation Cattle weight |
topic |
Multhiphasic growth models Stochastic differential equations Estimation Cattle weight |
description |
The evolution of the growth of an individual in a random environment can be described through stochastic differential equations of the form dY(t) = b(a-Y(t))dt+sdW(t), where Y(t)=h(X(t)), X(t) is the size of the individual at age t, h is a strictly increasing continuously differentiable function, a=h(A), where A is the average asymptotic size, and b represents the rate of approach to maturity. The parameter s measures the intensity of the effect of random fluctuations on growth and W(t) is the standard Wiener process. We have previously applied this monophasic model, in which there is only one functional form describing the average dynamics of the complete growth curve, and studied the estimation issues. Here, we present the generalization of the above stochastic model to the multiphasic case, in which we consider that the growth coefficient b assumes different values for different phases of the animal life. For simplicity, we consider two phases with growth coefficients b1 and b2. Results and methods are illustrated using bovine growth data. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-01-20T11:23:32Z 2011-01-20 2011-01-20T00:00:00Z |
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/10174/2476 http://hdl.handle.net/10174/2476 |
url |
http://hdl.handle.net/10174/2476 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Methodology and Computing in Applied Probability livre pasf@uevora.pt braumann@uevora.pt croquete@uevora.pt 336 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
154805 bytes 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 |
repository.mail.fl_str_mv |
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1799136465186193408 |