Generalized Linear Models

Ludwig Fahrmeir (), Thomas Kneib (), Stefan Lang () and Brian D. Marx ()
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Ludwig Fahrmeir: LMU Munich, Institute of Statistics
Thomas Kneib: University of Göttingen, Statistics and Econometrics
Stefan Lang: University of Innsbruck, Department of Statistics
Brian D. Marx: Louisiana State University

Chapter Chapter 5 in Regression, 2021, pp 283-342 from Springer

Abstract: Abstract Linear models are well suited for regression analyses when the response variable is continuous and at least approximately normal. In some cases, an appropriate transformation is needed to ensure approximate normality of the response. In addition, the expectation of the response is assumed to be a linear combination of covariates. Again, these covariates may be transformed before being included in the linear predictor. However, in many applications, the response is not a continuous variable, but rather binary, categorical, or a count variable as in the following examples.

Date: 2021
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DOI: 10.1007/978-3-662-63882-8_5

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