Modelling background noise in finite mixtures of generalized linear regression models

Authors

Friedrich Leisch, Ludwig Maximilians Universitat, Munchen

RIS ID

27035

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.

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.