The generalized linear model is a very important tool for analyzing real
data in several application domains where the relationship between the response and
explanatory variables may not be linear or the distributions may not be normal in all
the cases. Quite often such real data contain a significant number of outliers in relation
to the standard parametric model used in the analysis; in such cases inference based
on the maximum likelihood estimator could be unreliable. In this paper, we develop a
robust estimation procedure for the generalized linear models that can generate robust
estimators with little loss in efficiency. We will also explore two particular special
cases in detail—Poisson regression for count data and logistic regression for binary
data. We will also illustrate the performance of the proposed estimators through some
real-life examples.

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• Robust estimation in generalized linear models: the density power divergence approach