  Generalized Linear Models are routinely used in data analysis. The classical
procedures for estimation are based on Maximum Likelihood and it is well known
that the presence of outliers can have a large impact on this estimator. Robust
procedures are presented in the literature but they need a robust initial
estimate in order to be computed. This is especially important for robust
procedures with non convex loss function such as redescending M-estimators.
Subsampling techniques are often used to determine a robust initial estimate;
however when the number of unknown parameters is large the number of subsamples
needed in order to have a high probability of having one subsample free of
outliers become infeasible. Furthermore the subsampling procedure provides a
non deterministic starting point. Based on ideas in Pena and Yohai (1999), we
introduce a deterministic robust initial estimate for M-estimators based on
transformations Valdora and Yohai (2014) for which we also develop an
iteratively reweighted least squares algorithm. The new methods are studied by
Monte Carlo experiments.
