Title: Linear regression models with finite mixtures of skew heavy-tailed errors
Authors: Luis Enrique Benites Sanchez - University of Sao Paulo (Brazil)
Rocio Maehara Aliaga - University of Sao Paulo (Brazil)
Victor Hugo Lachos Davila - University of Connecticut (United States) [presenting]
Abstract: The aim is to estimate regression models whose error terms follow a finite mixture of scale mixtures of skew-normal (SMSN) distributions, a rich class of distributions that contains the skew-normal, skew-$t$, skew-slash and skew-contaminated normal distributions as proper elements. This approach allows us to model data with great flexibility, accommodating simultaneously multimodality, skewness and heavy tails. We developed a simple EM-type algorithm to perform maximum likelihood (ML) inference of the parameters of the proposed model with closed form expressions for both E- and M-steps. Furthermore, the empirical information matrix is derived analytically to account for standard errors. The practical utility of the new method is illustrated with the analysis of a real dataset and several simulation studies. The proposed algorithm and methods are implemented in the R package FMsmsnReg.