Modelling background noise in finite mixtures of generalized linear regression models
RIS ID
27035
Abstract
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.
Publication Details
Leisch, F. (2008). Modelling background noise in finite mixtures of generalized linear regression models. In P. Brito (Eds.), Compstat 2008 - Proceedings in Computational Statistics (pp. 385-396). Heidelberg, Germany: Physica Verlag.