Inference and diagnostics in skew scale mixtures of normal regression models

Autor(es)

Ferreira, Clecio S. ^[1] ; Lachos, Victor H. ^[2] ; Bolfarine, Heleno ^[3]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION; v. 85, n. 3, p. 517-537, FEB 11 2015.
• Citações Web of Science: 6

Resumo

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)

• Processo FAPESP: 11/17400-6 - Aplicações das distribuições de misturas da escala Skew-Normal em modelos de efeitos mistos
• Beneficiário: Víctor Hugo Lachos Dávila
• Modalidade de apoio: Auxílio à Pesquisa - Regular