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Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models

In: Compstat 2008

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  • Friedrich Leisch

    (Ludwig-Maximilians-Universität München, Department of Statistics)

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.

Suggested Citation

  • Friedrich Leisch, 2008. "Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 385-396, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2084-3_32
    DOI: 10.1007/978-3-7908-2084-3_32
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