Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/40287
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dc.creatorLiska, Gilberto Rodrigues-
dc.creatorCirillo, Marcelo Ângelo-
dc.creatorMenezes, Fortunato Silva de-
dc.creatorBueno Filho, Julio Silvio de Sousa-
dc.date.accessioned2020-04-23T17:46:55Z-
dc.date.available2020-04-23T17:46:55Z-
dc.date.issued2019-
dc.identifier.citationLISKA, G. R. et al. Machine learning based on extended generalized linear model applied in mixture experiments. Communications in Statistics - Simulation and Computation, London, 2019. DOI: 10.1080/03610918.2019.1697821pt_BR
dc.identifier.urihttps://www.tandfonline.com/doi/full/10.1080/03610918.2019.1697821pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/40287-
dc.description.abstractWhen 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.pt_BR
dc.languageenpt_BR
dc.publisherTaylor & Francis Grouppt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceCommunications in Statistics - Simulation and Computationpt_BR
dc.subjectDispersion modelpt_BR
dc.subjectBoosting algorithmpt_BR
dc.subjectMachine learningpt_BR
dc.subjectRegression modelingpt_BR
dc.subjectSimplex spacept_BR
dc.subjectModelo de dispersãopt_BR
dc.subjectAlgoritmo de reforçopt_BR
dc.subjectAprendizado de máquinapt_BR
dc.subjectModelos de regressãopt_BR
dc.subjectExperimentos de misturapt_BR
dc.titleMachine learning based on extended generalized linear model applied in mixture experimentspt_BR
dc.typeArtigopt_BR
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