Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models.
In: Brito, Paula (ed.) ,
Compstat 2008 - Proceedings in Computational Statistics. Heidelberg, Germany: Physica Verlag. pp. 385-396
In this paper we show how only a few outliers can completely break down EM-estimation of mixtures of regression models. A simple, yet very effective way of dealing with this problem, is to use a component where all regression parameters are fixed to zero to model the background noise. This noise component can be easily defined for different types of generalized linear models, has a familiar interpretation as the empty regression model, and is not very sensitive with respect to its own parameters.