Abstract
Generalized linear models (GLM) extend the concept of the well understood linear regression model. The linear model assumes that the conditional expectation of the dependent variable Y is equal to a linear combination of the explanatory variables X. Unfortunately, this restriction to linearity cannot take into account a variety of practical situations. A generalized linear model introduces a link function around the linear combination of the explanatory variables. That way also non-normal and discrete distributions of Y can be fitted within this model class.
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Müller, M. (2012). Generalized Linear Models. In: Gentle, J., Härdle, W., Mori, Y. (eds) Handbook of Computational Statistics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21551-3_24
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