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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Inference and diagnostics in skew scale mixtures of normal regression models

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Author(s):
Ferreira, Clecio S. [1] ; Lachos, Victor H. [2] ; Bolfarine, Heleno [3]
Total Authors: 3
Affiliation:
[1] Univ Fed Juiz de Fora, Dept Estat, Juiz De Fora - Brazil
[2] Univ Estadual Campinas, Dept Estat, Campinas, SP - Brazil
[3] Univ Sao Paulo, Dept Estat, Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION; v. 85, n. 3, p. 517-537, FEB 11 2015.
Web of Science Citations: 6
Abstract

Skew scale mixtures of normal distributions are often used for statistical procedures involving asymmetric data and heavy-tailed. The main virtue of the members of this family of distributions is that they are easy to simulate from and they also supply genuine expectation-maximization (EM) algorithms for maximum likelihood estimation. In this paper, we extend the EM algorithm for linear regression models and we develop diagnostics analyses via local influence and generalized leverage, following Zhu and Lee's approach. This is because Cook's well-known approach cannot be used to obtain measures of local influence. The EM-type algorithm has been discussed with an emphasis on the skew Student-t-normal, skew slash, skew-contaminated normal and skew power-exponential distributions. Finally, results obtained for a real data set are reported, illustrating the usefulness of the proposed method. (AU)

FAPESP's process: 11/17400-6 - Applications of the scale mixture of Skew-Normal distributions in linear mixed effects models
Grantee:Víctor Hugo Lachos Dávila
Support type: Regular Research Grants