Resumen
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, skewt,
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.
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 original | Inglés |
---|---|
Páginas (desde-hasta) | 21-40 |
Número de páginas | 20 |
Publicación | Chilean Journal of Statistics |
Volumen | 10 |
N.º | 1 |
Estado | Publicada - 15 abr. 2019 |
Nota bibliográfica
Funding Information:The authors thank the Editors and Reviewers for their constructive comments on an earlier version of this manuscript. Luis Benites and Roćıo Maehara acknowledges support from CNPq-Brazil. Victor H. Lachos was supported from CNPq-Brazil (Grant 306334/2015-1). Partial support from CAPES is also acknowledged.
Publisher Copyright:
Chilean Statistical Society – Sociedad Chilena de Estadística.