Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/82056
Type: Artigo de periódico
Title: Multivariate mixture modeling using skew-normal independent distributions
Author: Cabral, CRB
Lachos, VH
Prates, MO
Abstract: In this paper we consider a flexible class of models, with elements that are finite mixtures of multivariate skew-normal independent distributions. A general EM-type algorithm is employed for iteratively computing parameter estimates and this is discussed with emphasis on finite mixtures of skew-normal, skew-t, skew-slash and skew-contaminated normal distributions. Further, a general information-based method for approximating the asymptotic covariance matrix of the estimates is also presented. The accuracy of the associated estimates and the efficiency of some information criteria are evaluated via simulation studies. Results obtained from the analysis of artificial and real data sets are reported illustrating the usefulness of the proposed methodology. The proposed EM-type algorithm and methods are implemented in the R package mixsmsn. (C) 2011 Elsevier B.V. All rights reserved.
Subject: EM algorithm
Multivariate finite mixtures
Skew-normal distribution
Skew-normal independent distributions
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Identifier DOI: 10.1016/j.csda.2011.06.026
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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