We study influence diagnostics for generalized linear models when the
true covariates are unobservable but measured with error. Based on the
bias-corrected estimation of model parameters, diagnostic measures ar
e developed to identify outlying and influential observations. The mag
nitude of influence is then assessed via a simulated envelope approach
. The proposed diagnostic procedure is illustrated on two examples.