Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/1089
Type: Artigo de periódico
Title: Multivariate measurement error models based on scale mixtures of the skew-normal distribution
Author: LACHOS, V. H.
LABRA, F. V.
BOLFARINE, H.
GHOSH, Pulak
Abstract: Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.
Subject: EM algorithm
scale mixtures of the skew-normal distribution
Mahalanobis distance
measurement error models
Country: Inglaterra
Editor: TAYLOR & FRANCIS LTD
Citation: STATISTICS, v.44, n.6, p.541-556, 2010
Rights: fechado
Identifier DOI: 10.1080/02331880903236926
Address: http://apps.isiknowledge.com/InboundService.do?Func=Frame&product=WOS&action=retrieve&SrcApp=EndNote&UT=000284630600001&Init=Yes&SrcAuth=ResearchSoft&mode=FullRecord
http://dx.doi.org/10.1080/02331880903236926
Date Issue: 2010
Appears in Collections:IMECC - Artigos e Outros Documentos

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