Robust mixture regression modeling based on scale mixtures of skew-normal distributions

Autor(es)

Zeller, Camila B. ^[1] ; Cabral, Celso R. B. ^[2] ; Lachos, Victor H. ^[3]

Afiliação do(s) autor(es)

^[1] Univ Fed Juiz de Fora, Dept Estat, Cidade Univ, Juiz De Fora, MG - Brazil

^[2] Univ Fed Amazonas, Dept Estat, Manaus, Amazonas - Brazil

^[3] Univ Estadual Campinas, Dept Estat, Cidade Univ Zeferino Vaz, Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: TEST; v. 25, n. 2, p. 375-396, JUN 2016.
• Citações Web of Science: 6

Resumo

The traditional estimation of mixture regression models is based on the assumption of normality (symmetry) of component errors and thus is sensitive to outliers, heavy-tailed errors and/or asymmetric errors. In this work we present a proposal to deal with these issues simultaneously in the context of the mixture regression by extending the classic normal model by assuming that the random errors follow a scale mixtures of skew-normal distributions. This approach allows us to model data with great flexibility, accommodating skewness and heavy tails. The main virtue of considering the mixture regression models under the class of scale mixtures of skew-normal distributions is that they have a nice hierarchical representation which allows easy implementation of inference. We develop a simple EM-type algorithm to perform maximum likelihood inference of the parameters of the proposed model. In order to examine the robust aspect of this flexible model against outlying observations, some simulation studies are also presented. Finally, a real data set is analyzed, illustrating the usefulness of the proposed method. (AU)

• Processo FAPESP: 14/02938-9 - Estimação e diagnóstico em modelos de efeitos mistos para dados censurados usando misturas de escala skew-normal
• Beneficiário: Víctor Hugo Lachos Dávila
• Modalidade de apoio: Auxílio à Pesquisa - Regular