Rick,

In practical applications with Logistic regressions I have used DFBETAS and have explored a number of alternatives. I have settled on elimination of observations if the observation is influential for more than half of the features in my model. I also push the cut off out beyond the usual xcutoff = 2 / sqrt(n); essentially I am concluding that this cut off is to harsh.

The model is estimated initially to produce the dfbtas, then re-estimated on the training set with the influential observations removed. The impact on the AUR has been significant. The models produced from this approach have been commercially successful where the previous models were not.

There is considerable debate about elimination of data points and many articles I have read would shun my approach.

I would be interested to hear your thoughts on this approach and the debate.