Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models

Leisch, Friedrich (2008): 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

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.

• Item Type: Book Section
• Keywords: mixture models, generalized linear models, robust statistics, R
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• ISBN: 978-3-7908-2083-6
• Place of Publication: Heidelberg, Germany
• Language: German