Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/2379
Type: Artigo de periódico
Title: Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions
Author: Labra, Filidor V.
Garay, Aldo M.
Lachos, Victor H.
Ortega, Edwin M. M.
Abstract: An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.
Subject: Case-deletion model
EM algorithm
Homogeneity
Local influence
Nonlinear regression models
Scale mixtures of skew-normal distributions
Editor: Elsevier
Citation: Journal of Statistical Planning and Inference. Elsevier, v.142, n.7, p.2149-2165, 2012
Rights: fechado
Identifier DOI: 10.1016/j.jspi.2012.02.018
Date Issue: 2012
Appears in Collections:IMECC - Artigos e Outros Documentos

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