Machine learning based on extended generalized linear model applied in mixture experiments

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorLiska, Gilberto Rodrigues-
Autor(es): dc.creatorCirillo, Marcelo Ângelo-
Autor(es): dc.creatorMenezes, Fortunato Silva de-
Autor(es): dc.creatorBueno Filho, Julio Silvio de Sousa-
Data de aceite: dc.date.accessioned2026-02-09T11:35:15Z-
Data de disponibilização: dc.date.available2026-02-09T11:35:15Z-
Data de envio: dc.date.issued2020-04-23-
Data de envio: dc.date.issued2020-04-23-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/40287-
Fonte completa do material: dc.identifierhttps://www.tandfonline.com/doi/full/10.1080/03610918.2019.1697821-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1142958-
Descrição: dc.descriptionWhen performing mixture experiments, we observe that maximum likelihood methods present problems related to the collinearity, small sample size, and over/under dispersion. In order to overcome these problems, this investigation proposes a model built in accordance with a machine learning approach. This approach will be called Boosted Simplex Regression, which has been evaluated both in terms of accuracy and precision for the odds ratio. The advantages of this new approach are illustrated in a mixture experiment, which has made us conclude that the model Boosted Simplex Regression has unveiled not only better fit quality but also more precise odds ratio confidence intervals.-
Idioma: dc.languageen-
Publicador: dc.publisherTaylor & Francis Group-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceCommunications in Statistics - Simulation and Computation-
Palavras-chave: dc.subjectDispersion model-
Palavras-chave: dc.subjectBoosting algorithm-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectRegression modeling-
Palavras-chave: dc.subjectSimplex space-
Palavras-chave: dc.subjectModelo de dispersão-
Palavras-chave: dc.subjectAlgoritmo de reforço-
Palavras-chave: dc.subjectAprendizado de máquina-
Palavras-chave: dc.subjectModelos de regressão-
Palavras-chave: dc.subjectExperimentos de mistura-
Título: dc.titleMachine learning based on extended generalized linear model applied in mixture experiments-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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