The use of mixtures for dealing with non-normal regression errors

In many situations, the distribution of the error terms of a linear regression model departs significantly from normality. It is shown, through a simulation study. that an effective strategy to deal with these situations is fitting a regression model based on the assumption that the error terms follow a mixture of normal distributions. The main advantage. with respect to the usual approach based on the least-squares method is a greater precision of the parameter estimates and confidence intervals. For the parameter estimation we make use of the EM algorithm. while confidence intervals are constructed through a bootstrap method. (C) 2004 Elsevier B.V. All rights reserved.