Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/104917
Type: Artigo de periódico
Title: Influence Diagnostics For Skew-normal Linear Mixed Models
Author: Bolfarine H.
Montenegro L.C.
Lachos V.H.
Abstract: Normality (symmetry) of the random effects is a routine assumption in linear mixed models but it may, sometimes, be unrealistic, obscuring important features of among-subjects variation. We relax this assumption by assuming that the random effects density is skew-normal, considered as an extension of the univariate version proposed by Sahu, Dey and Branco (CJS, 2003). Following Zhu and Lee (JRSSB, 2001), we implement an EM-type algorithm to parameter estimation and then using the related conditional expectation of the complete-data log-likelihood function, develop diagnostic measures for implementing the local influence approach under four model perturbation schemes. Results obtained from simulated and real data sets are reported illustrating the usefulness of the approach. © 2007, Indian Statistical Institute.
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Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-58149478139&partnerID=40&md5=388d5f0c394a6796863cf0df1f44f171
Date Issue: 2007
Appears in Collections:Unicamp - Artigos e Outros Documentos

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