Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10328157 | Computational Statistics & Data Analysis | 2005 | 14 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
F. Bartolucci, L. Scaccia,