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In this paper we suggest a simple robust method for the detection
of atypical and influential observations in binomial data. Our
technique is based on a forward search procedure which orders the
observations from those most in agreement with a specified
generalized linear model to those least in agreement with it. The
effectiveness of the forward search estimator in detecting masked
multiple outliers, and more generally in ordering binomial data,
is shown by means of three data sets. Plots of diagnostic
quantities during the forward search clearly show the effect of
individual observations on residuals and test statistics. These
examples reveal the strength of our method in getting inside the
data in a way which is more simple and effective than it would be
using standard deletion diagnostic procedures.
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