  In this paper we propose a procedure for robust estimation in the context of
generalized linear models based on the maximum Lq-likelihood method. Alongside
this, an estimation algorithm that represents a natural extension of the usual
iteratively weighted least squares method in generalized linear models is
presented. It is through the discussion of the asymptotic distribution of the
proposed estimator and a set of statistics for testing linear hypothesis that
it is possible to define standardized residuals using the mean-shift outlier
model. In addition, robust versions of deviance function and the Akaike
information criterion are defined with the aim of providing tools for model
selection. Finally, the performance of the proposed methodology is illustrated
through a simulation study and analysis of a real dataset.
