Brasil | Tesis
dc.contributorNovelli, Cibele Maria Russo
dc.contributorhttp://lattes.cnpq.br/1011098065426388
dc.contributorhttp://lattes.cnpq.br/7522100387675610
dc.creatorGomes, José Clelto Barros
dc.date.accessioned2020-03-11T12:28:10Z
dc.date.accessioned2022-10-10T21:30:32Z
dc.date.available2020-03-11T12:28:10Z
dc.date.available2022-10-10T21:30:32Z
dc.date.created2020-03-11T12:28:10Z
dc.date.issued2019-12-05
dc.identifierGOMES, José Clelto Barros. Métodos de estimação em modelos de efeitos mistos não lineares de caudas pesadas. 2019. Tese (Doutorado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2019. Disponível em: https://repositorio.ufscar.br/handle/ufscar/12312.
dc.identifierhttps://repositorio.ufscar.br/handle/ufscar/12312
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/4042804
dc.description.abstractParameter estimation in nonlinear mixed-effects models is often challenging. In this thesis, a comparison of estimation methods for these models is proposed under a frequentist approach. In the first study, a comparison of maximum likelihood estimates under an exact method via Monte Carlo expectation-maximization (MCEM) and an approximate method based on a Taylor expansion, frequently used in the literature, is provided. In a second study, a restricted maximum likelihood estimation method is proposed, aiming to decrease the bias for the variance components estimates, based on the integration of the likelihood function on the fixed-effects, also in an exact likelihood context. These estimates are compared to the maximum likelihood ones. For the latter comparison, stochastic approximation of expectation-maximization (SAEM) algorithms are considered. The random effects and errors are assumed to follow multivariate symmetric distributions, namely the scale mixture of normal distributions, which include the normal, t and slash distributions. Finally, a general nonlinear mixed-effects model is proposed, where no linear relation is assumed in the random effects structure. In all the proposals, real data sets and simulation studies are used to illustrate the estimates’ properties.
dc.languageeng
dc.publisherUniversidade Federal de São Carlos
dc.publisherUFSCar
dc.publisherPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEs
dc.publisherCâmpus São Carlos
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/br/
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Brazil
dc.subjectNonlinear models
dc.subjectMixed-effects models
dc.subjectCorrelated data
dc.subjectEstimation methods
dc.subjectMaximum likelihood
dc.subjectRestricted maximum likelihood
dc.subjectModelos não-lineares
dc.subjectModelos mistos
dc.subjectDados correlacionados
dc.subjectMétodos de estimação
dc.subjectMáxima verossimilhança
dc.subjectMáxima verossimilhança restrita
dc.titleMétodos de estimação em modelos de efeitos mistos não lineares de caudas pesadas
dc.typeTesis


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