Likelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads dataset
Ano de defesa: | 2019 |
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Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | eng |
Instituição de defesa: |
Universidade Federal de Pernambuco
|
Programa de Pós-Graduação: |
Programa de Pos Graduacao em Estatistica
|
Departamento: |
Não Informado pela instituição
|
País: |
Brasil
|
Palavras-chave em Português: | |
Link de acesso: | https://repositorio.ufpe.br/handle/123456789/33518 |
Resumo: | In AIDS clinical trials, the HIV-1 RNA measurements are often subject to some upper and lower detection limits, depending on the quantification assays. Linear and nonlinear mixedeffects models, with modifications to accommodate censored observations, are routinely used to analyze this type of data (VAIDA; LIU, 2009). This work presents a likelihood based approach for fitting Linear and nonlinear mixedeffects models, with modifications to accommodate censored observations and considering an structure autoregressive of order p (AR(p)) dependence on the error term. An EM-type algorithm is developed for computing the maximum likelihood estimates, obtaining as a byproduct the standard errors of the fixed effects and the likelihood value. Moreover, the constraints on the parameter space arising, from the stationarity conditions for the autoregressive parameters, in the EM algorithm are handled by a reparametrization scheme, as discussed by Lin e Lee (2007). Finally, the proposed algorithm is implemented in the R package ARpMMEC, which is available. It presents an application to real data and developed three simulation studies that show the relevance and applicability of the proposed model. |
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network_name_str |
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OLIVARI, Rommy Camascahttp://lattes.cnpq.br/3971746603022796http://lattes.cnpq.br/6628260142102150GARAY, Aldo William MedinaLACHOS DAVILA, Víctor Hugo2019-09-23T19:38:51Z2019-09-23T19:38:51Z2019-02-27https://repositorio.ufpe.br/handle/123456789/33518In AIDS clinical trials, the HIV-1 RNA measurements are often subject to some upper and lower detection limits, depending on the quantification assays. Linear and nonlinear mixedeffects models, with modifications to accommodate censored observations, are routinely used to analyze this type of data (VAIDA; LIU, 2009). This work presents a likelihood based approach for fitting Linear and nonlinear mixedeffects models, with modifications to accommodate censored observations and considering an structure autoregressive of order p (AR(p)) dependence on the error term. An EM-type algorithm is developed for computing the maximum likelihood estimates, obtaining as a byproduct the standard errors of the fixed effects and the likelihood value. Moreover, the constraints on the parameter space arising, from the stationarity conditions for the autoregressive parameters, in the EM algorithm are handled by a reparametrization scheme, as discussed by Lin e Lee (2007). Finally, the proposed algorithm is implemented in the R package ARpMMEC, which is available. It presents an application to real data and developed three simulation studies that show the relevance and applicability of the proposed model.Ensaios clínicos de HIV, as medições de VIH-1 RNA são frequentemente restritas por alguns limites de detecção superiores e inferiores, dependendo do ensaio de quantificação. Modelos com efeitos mistos, lineares e não lineares, adaptados para observações censuradas, são rotineiramente utilizados para analisar este tipo de dados (VAIDA; LIU, 2009). Este trabalho apresenta, sobre uma abordagem baseada na maximização da função de verossimilhança, o ajuste dos modelos com efeitos mistos, lineares e não lineares, adaptados para observações censuradas e considerando uma estrutura de dependência autorregressiva de ordem p (AR(p)) para o erro do modelo. É desenvolvido um algoritmo tipo EM para calcular as estimativas por máxima verossimilhança, obtendo como resultado os erros padrões dos efeitos fixos e o valor da função de verossimilhança. Além disso, para lidiar com as restrições sobre o espaço paramétrico, que surgem na estimação dos parâmetros autorregresivos do modelo AR(p) no algoritmo EM, é utilizado um esquema de reparametrização, como discutido por Lin e Lee (2007). Finalmente, o algoritmo proposto é implementado no pacote ARpLMEC, o qual está disponível. É apresentada uma aplicação a dados reais e desenvolvido três estudos de simulação que evidenciam a relevância e aplicabilidade do modelo proposto.engUniversidade Federal de PernambucoPrograma de Pos Graduacao em EstatisticaUFPEBrasilAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessProbabilidade e estatísticaModelos AR(p) autorregressivoDados censuradosLikelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads datasetinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesismestradoreponame:Repositório Institucional da UFPEinstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPETHUMBNAILDISSERTAÇÃO Rommy Camasca Olivari.pdf.jpgDISSERTAÇÃO Rommy Camasca Olivari.pdf.jpgGenerated Thumbnailimage/jpeg1220https://repositorio.ufpe.br/bitstream/123456789/33518/5/DISSERTA%c3%87%c3%83O%20Rommy%20Camasca%20Olivari.pdf.jpge57dbf7074aad28a427b59301b9de2edMD55ORIGINALDISSERTAÇÃO Rommy Camasca Olivari.pdfDISSERTAÇÃO Rommy Camasca Olivari.pdfapplication/pdf1652857https://repositorio.ufpe.br/bitstream/123456789/33518/1/DISSERTA%c3%87%c3%83O%20Rommy%20Camasca%20Olivari.pdf99cfc7f1745378b9d1ef674bf339a8beMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.pt_BR.fl_str_mv |
Likelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads dataset |
title |
Likelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads dataset |
spellingShingle |
Likelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads dataset OLIVARI, Rommy Camasca Probabilidade e estatística Modelos AR(p) autorregressivo Dados censurados |
title_short |
Likelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads dataset |
title_full |
Likelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads dataset |
title_fullStr |
Likelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads dataset |
title_full_unstemmed |
Likelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads dataset |
title_sort |
Likelihood based inference for autoregressive censored mixed-effects models, with applications to hiv viral loads dataset |
author |
OLIVARI, Rommy Camasca |
author_facet |
OLIVARI, Rommy Camasca |
author_role |
author |
dc.contributor.authorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/3971746603022796 |
dc.contributor.advisorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/6628260142102150 |
dc.contributor.author.fl_str_mv |
OLIVARI, Rommy Camasca |
dc.contributor.advisor1.fl_str_mv |
GARAY, Aldo William Medina |
dc.contributor.advisor-co1.fl_str_mv |
LACHOS DAVILA, Víctor Hugo |
contributor_str_mv |
GARAY, Aldo William Medina LACHOS DAVILA, Víctor Hugo |
dc.subject.por.fl_str_mv |
Probabilidade e estatística Modelos AR(p) autorregressivo Dados censurados |
topic |
Probabilidade e estatística Modelos AR(p) autorregressivo Dados censurados |
description |
In AIDS clinical trials, the HIV-1 RNA measurements are often subject to some upper and lower detection limits, depending on the quantification assays. Linear and nonlinear mixedeffects models, with modifications to accommodate censored observations, are routinely used to analyze this type of data (VAIDA; LIU, 2009). This work presents a likelihood based approach for fitting Linear and nonlinear mixedeffects models, with modifications to accommodate censored observations and considering an structure autoregressive of order p (AR(p)) dependence on the error term. An EM-type algorithm is developed for computing the maximum likelihood estimates, obtaining as a byproduct the standard errors of the fixed effects and the likelihood value. Moreover, the constraints on the parameter space arising, from the stationarity conditions for the autoregressive parameters, in the EM algorithm are handled by a reparametrization scheme, as discussed by Lin e Lee (2007). Finally, the proposed algorithm is implemented in the R package ARpMMEC, which is available. It presents an application to real data and developed three simulation studies that show the relevance and applicability of the proposed model. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-09-23T19:38:51Z |
dc.date.available.fl_str_mv |
2019-09-23T19:38:51Z |
dc.date.issued.fl_str_mv |
2019-02-27 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufpe.br/handle/123456789/33518 |
url |
https://repositorio.ufpe.br/handle/123456789/33518 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.publisher.program.fl_str_mv |
Programa de Pos Graduacao em Estatistica |
dc.publisher.initials.fl_str_mv |
UFPE |
dc.publisher.country.fl_str_mv |
Brasil |
publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
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reponame:Repositório Institucional da UFPE instname:Universidade Federal de Pernambuco (UFPE) instacron:UFPE |
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UFPE |
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UFPE |
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Repositório Institucional da UFPE |
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Repositório Institucional da UFPE |
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