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Grün, Bettina and Leisch, Friedrich (2007): Finite Mixtures of Generalized Linear Regression Models. Department of Statistics: Technical Reports, No.13 [PDF, 498kB]


Generalized linear models have become a standard technique in the statistical modelling toolbox for investigating relationships between variables. The assumption of homogeneity of regression coefficients over all observations can be relaxed by incorporating generalized linear models into the finite mixture framework.

The model class consisting of finite mixtures of generalized linear models is presented. Model identification is discussed given that difficulties might be encountered due to trivial and generic identifiability problems. These problems have already been observed for mixtures of distributions, but the extension to mixtures of regression models introduces additional identifiability problems. Details on model estimation are given and the application is illustrated on several examples.

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