Author:

Keywords:

Model, Data

Abstract:

Abstract: In this note we discuss the breakdown behavior of the Maximum Likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to infinity, but rather breaks down to zero when adding severe outliers to a data set. Numerical experiments confirm this behavior. As a more robust alternative, a Weighted Maximum Likelihood (WML) estimator will be considered.