Multiphasic individual growth models in random environments

Detalhes bibliográficos
Autor(a) principal: Filipe, Patrícia A.
Data de Publicação: 2011
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/10174/2476
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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spelling 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
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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
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