Multiphasic Individual Growth Models in Random Environments

Detalhes bibliográficos
Autor(a) principal: Filipe, Patrícia A.
Data de Publicação: 2012
Outros Autores: Braumann, Carlos A., Roquete, Carlos J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/7122
https://doi.org/10.1007/s11009-010-9172-0
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) = β(α − Y(t))dt + σdW(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, α = h(A), where A is the average asymptotic size, and β represents the rate of approach to maturity. The parameter σ 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 β assumes different values for different phases of the animal’s life. For simplicity, we consider two phases with growth coefficients β1 and β2. Results and methods are illustrated using bovine growth data.
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spelling Multiphasic Individual Growth Models in Random EnvironmentsMultiphasic 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) = β(α − Y(t))dt + σdW(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, α = h(A), where A is the average asymptotic size, and β represents the rate of approach to maturity. The parameter σ 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 β assumes different values for different phases of the animal’s life. For simplicity, we consider two phases with growth coefficients β1 and β2. Results and methods are illustrated using bovine growth data.2013-01-08T12:29:32Z2013-01-082012-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/7122http://hdl.handle.net/10174/7122https://doi.org/10.1007/s11009-010-9172-0engCIMApasf@uevora.ptbraumann@uevora.ptcroquete@uevora.pt340Filipe, 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:RCAAP2024-01-03T18:47:04Zoai:dspace.uevora.pt:10174/7122Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:01:43.521246Repositório 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.
Multiphasic 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 Multiphasic growth models
Stochastic differential equations
Estimation
Cattle weight
topic Multiphasic 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) = β(α − Y(t))dt + σdW(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, α = h(A), where A is the average asymptotic size, and β represents the rate of approach to maturity. The parameter σ 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 β assumes different values for different phases of the animal’s life. For simplicity, we consider two phases with growth coefficients β1 and β2. Results and methods are illustrated using bovine growth data.
publishDate 2012
dc.date.none.fl_str_mv 2012-03-01T00:00:00Z
2013-01-08T12:29:32Z
2013-01-08
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/10174/7122
http://hdl.handle.net/10174/7122
https://doi.org/10.1007/s11009-010-9172-0
url http://hdl.handle.net/10174/7122
https://doi.org/10.1007/s11009-010-9172-0
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv CIMA
pasf@uevora.pt
braumann@uevora.pt
croquete@uevora.pt
340
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