Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/68300
Type: Artigo de periódico
Title: Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions
Author: Lachos, VH
Bandyopadhyay, D
Garay, AM
Abstract: An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. We derive a simple EM-type algorithm for iteratively computing maximum likelihood (ML) estimates and the observed information matrix is derived analytically. Simulation studies demonstrate the robustness of this flexible class against outlying and influential observations, as well as nice asymptotic properties of the proposed EM-type ML estimates. Finally, the methodology is illustrated using an ultrasonic calibration data. (C) 2011 Elsevier B.V. All rights reserved.
Subject: EM algorithm
Homogeneity
Nonlinear regression models
Scale mixtures
Skew-normal
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Identifier DOI: 10.1016/j.spl.2011.03.019
Date Issue: 2011
Appears in Collections:Unicamp - Artigos e Outros Documentos

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