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dc.contributor.authorSantos, Helton Saulo Bezerra dos-
dc.contributor.authorGabriel, Roberto Vila-
dc.contributor.authorSouza, Rubens Batista de-
dc.date.accessioned2025-12-03T14:01:11Z-
dc.date.available2025-12-03T14:01:11Z-
dc.date.issued2024-10-05-
dc.identifier.citationSANTOS, Helton Saulo Bezerra dos; GABRIEL, Roberto Vila; SOUZA, Rubens Batista de. Bivariate log-symmetric regression models applied to newborn data. Symmetry, Basel, v. 16, n. 10, e1315, 2024. DOI: https://doi.org/10.3390/sym16101315. Disponível em: https://www.mdpi.com/2073-8994/16/10/1315. Acesso em: 24 nov. 2025.pt_BR
dc.identifier.urihttp://repositorio.unb.br/handle/10482/53326-
dc.language.isoengpt_BR
dc.publisherMDPIpt_BR
dc.rightsAcesso Abertopt_BR
dc.titleBivariate log-symmetric regression models applied to newborn datapt_BR
dc.typeArtigopt_BR
dc.subject.keywordDispersão variávelpt_BR
dc.subject.keywordCorrelação variávelpt_BR
dc.subject.keywordRegressão linearpt_BR
dc.subject.keywordRecém-nascidospt_BR
dc.identifier.doihttps://doi.org/10.3390/sym16101315pt_BR
dc.relation.publisherversionLicensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).pt_BR
dc.description.abstract1This paper introduces bivariate log-symmetric models for analyzing the relationship between two variables, assuming a family of log-symmetric distributions. These models offer greater flexibility than the bivariate lognormal distribution, allowing for better representation of diverse distribution shapes and behaviors in the data. The log-symmetric distribution family is widely used in various scientific fields and includes distributions such as log-normal, log-Student-t, and log-Laplace, among others, providing several options for modeling different data types. However, there are few approaches to jointly model continuous positive and explanatory variables in regression analysis. Therefore, we propose a class of generalized linear model (GLM) regression models based on bivariate log-symmetric distributions, aiming to fill this gap. Furthermore, in the proposed model, covariates are used to describe its dispersion and correlation parameters. This study uses a dataset of anthropometric measurements of newborns to correlate them with various biological factors, proposing bivariate regression models to account for the relationships observed in the data. Such models are crucial for preventing and controlling public health issues.pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-4467-8652pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-1073-0114pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-1854-5805pt_BR
dc.contributor.affiliationUniversidade de Brasília, Department of Statisticspt_BR
dc.contributor.affiliationFederal University of Pelotas, Department of Economicspt_BR
dc.contributor.affiliationUniversidade de Brasília, Department of Statisticspt_BR
dc.contributor.affiliationUniversidade de Brasília, Department of Statisticspt_BR
dc.description.unidadeInstituto de Ciências Exatas (IE)pt_BR
dc.description.unidadeDepartamento de Estatística (IE EST)pt_BR
dc.description.ppgPrograma de Pós-Graduação em Estatísticapt_BR
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