Linear regression models using finite mixtures of skew heavy-tailed distributions

Luis Benites, Rocío Maehara, Victor H. Lachos, Heleno Bolfarine

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

3 Citas (Scopus)


In this paper, we propose a regression model based on the assumption that the error term follows a mixture of normal distributions. Specifically, we consider a finite scale mixture of skew-normal distributions, a rich family that contains the skew-normal, skew-t, skew-slash and skew-contaminated normal distributions as members. This model allows us to describe data with high flexibility, simultaneously accommodating multimodality, skewness and heavy tails. We develop a simple EM-type algorithm to perform maximum likelihood inference of the parameters of the proposed model with closed-form expressions for both E- and M-steps. Furthermore, the observed information matrix is derived analytically to account for the corresponding standard errors and a bootstrap procedure is implemented to test the number of components in the mixture. The practical utility of the new model is illustrated with a real dataset and several simulation studies. The proposed algorithm and methods are implemented in an R package named FMsmsnReg.

Idioma originalInglés
Páginas (desde-hasta)21-40
Número de páginas20
PublicaciónChilean Journal of Statistics
EstadoPublicada - abr. 2019


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