Brasil | Tesis
dc.contributorLouzada Neto, Francisco
dc.contributorhttp://lattes.cnpq.br/0994050156415890
dc.contributorhttp://lattes.cnpq.br/4655747321002185
dc.creatorShimizu, Taciana Kisaki Oliveira
dc.date.accessioned2019-02-27T17:19:46Z
dc.date.available2019-02-27T17:19:46Z
dc.date.created2019-02-27T17:19:46Z
dc.date.issued2018-12-10
dc.identifierSHIMIZU, Taciana Kisaki Oliveira. Penalized regression methods for compositional data. 2018. Tese (Doutorado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2018. Disponível em: https://repositorio.ufscar.br/handle/ufscar/11034.
dc.identifierhttps://repositorio.ufscar.br/handle/ufscar/11034
dc.description.abstractCompositional data consist of known vectors such as compositions whose components are positive and defined in the interval (0,1) representing proportions or fractions of a "whole", where the sum of these components must be equal to one. Compositional data is present in different areas, such as in geology, ecology, economy, medicine, among many others. Thus, there is great interest in new modeling approaches for compositional data, mainly when there is an influence of covariates in this type of data. In this context, the main objective of this thesis is to address the new approach of regression models applied in compositional data. The main idea consists of developing a marked method by penalized regression, in particular the Lasso (least absolute shrinkage and selection operator), elastic net and Spike-and-Slab Lasso (SSL) for the estimation of parameters of the models. In particular, we envision developing this modeling for compositional data, when the number of explanatory variables exceeds the number of observations in the presence of large databases, and when there are constraints on the dependent variables and covariates.
dc.languageeng
dc.publisherUniversidade Federal de São Carlos
dc.publisherUFSCar
dc.publisherPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEs
dc.publisherCâmpus São Carlos
dc.rightsAcesso aberto
dc.subjectDados composicionais
dc.subjectModelo de regressão
dc.subjectCoordenadas log-razão isométricas
dc.subjectSeleção de variáveis
dc.subjectCompositional data
dc.subjectRegression model
dc.subjectIsometric logratio coordinates
dc.subjectVariable selection
dc.titlePenalized regression methods for compositional data
dc.typeTesis


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