Brasil | Artículos de revistas
dc.creatorBasso, RM
dc.creatorLachos, VH
dc.creatorCabral, CRB
dc.creatorGhosh, P
dc.date2010
dc.dateDEC 1
dc.date2014-11-17T20:46:39Z
dc.date2015-11-26T17:42:15Z
dc.date2014-11-17T20:46:39Z
dc.date2015-11-26T17:42:15Z
dc.date.accessioned2018-03-29T00:24:05Z
dc.date.available2018-03-29T00:24:05Z
dc.identifierComputational Statistics & Data Analysis. Elsevier Science Bv, v. 54, n. 12, n. 2926, n. 2941, 2010.
dc.identifier0167-9473
dc.identifierWOS:000281333900005
dc.identifier10.1016/j.csda.2009.09.031
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/71180
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/71180
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/71180
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1287233
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionA flexible class of probability distributions, convenient for modeling data with skewness behavior, discrepant observations and population heterogeneity is presented. The elements of this family are convex linear combinations of densities that are scale mixtures of skew-normal distributions. An EM-type algorithm for maximum likelihood estimation is developed and the observed information matrix is obtained. These procedures are discussed with emphasis on finite mixtures of skew-normal, skew-t, skew-slash and skew contaminated normal distributions. In order to examine the performance of the proposed methods, some simulation studies are presented to show the advantage of this flexible class in clustering heterogeneous data and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. A real data set is analyzed, illustrating the usefulness of the proposed methodology. (C) 2009 Elsevier B.V. All rights reserved.
dc.description54
dc.description12
dc.descriptionSI
dc.description2926
dc.description2941
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationComputational Statistics & Data Analysis
dc.relationComput. Stat. Data Anal.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectMaximum-likelihood-estimation
dc.subjectEm Algorithm
dc.subjectT-distribution
dc.subjectConvergence
dc.subjectEcm
dc.titleRobust mixture modeling based on scale mixtures of skew-normal distributions
dc.typeArtículos de revistas


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