Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/71180
Type: Artigo
Title: Robust mixture modeling based on scale mixtures of skew-normal distributions
Author: Basso, Rodrigo M.
Lachos, Víctor H.
Cabral, Celso Rômulo Barbosa
Ghosh, Pulak
Abstract: A 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.
A 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 m
Subject: Distribuição normal assimétrica
Algoritmos de esperança-maximização
Misturas de escala (Estatística)
Estimação de máxima verossimilhança
Country: Holanda
Editor: Elsevier
Citation: Computational Statistics & Data Analysis. Elsevier Science Bv, v. 54, n. 12, n. 2926, n. 2941, 2010.
Rights: fechado
Identifier DOI: 10.1016/j.csda.2009.09.031
Address: https://www.sciencedirect.com/science/article/pii/S0167947309003612
Date Issue: 2010
Appears in Collections:IMECC - Artigos e Outros Documentos

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