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Generalized Linear Models

In: Regression

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ludwig Fahrmeir & Thomas Kneib & Stefan Lang & Brian D. Marx, 2021. "Generalized Linear Models," Springer Books, in: Regression, edition 2, chapter 0, pages 283-342, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-63882-8_5
    DOI: 10.1007/978-3-662-63882-8_5
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