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Type: Artigo de periódico
Title: On diagnostics in multivariate measurement error models under asymmetric heavy-tailed distributions
Author: Zeller, CB
Carvalho, RR
Lachos, VH
Abstract: In this paper, we discuss the extension of some diagnostic procedures to multivariate measurement error models with scale mixtures of skew-normal distributions (Lachos et al., Statistics 44:541-556, 2010c). This class provides a useful generalization of normal (and skew-normal) measurement error models since the random term distributions cover symmetric, asymmetric and heavy-tailed distributions, such as skew-t, skew-slash and skew-contaminated normal, among others. Inspired by the EM algorithm proposed by Lachos et al. (Statistics 44:541-556, 2010c), we develop a local influence analysis for measurement error models, following Zhu and Lee's (J R Stat Soc B 63:111-126, 2001) approach. This is because the observed data log-likelihood function associated with the proposed model is somewhat complex and Cook's well-known approach can be very difficult to apply to achieve local influence measures. Some useful perturbation schemes are also discussed. In addition, a score test for assessing the homogeneity of the skewness parameter vector is presented. Finally, the methodology is exemplified through a real data set, illustrating the usefulness of the proposed methodology.
Subject: EM algorithm
Local influence
Mahalanobis distance
Measurement error models
Scale mixtures of skew-normal distributions
Country: EUA
Editor: Springer
Rights: fechado
Identifier DOI: 10.1007/s00362-011-0371-8
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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