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Generalized Linear Models

In: Regression

Author

Listed:
  • Ludwig Fahrmeir

    (University of Munich, Department of Statistics)

  • Thomas Kneib

    (University of Göttingen, Chair of Statistics)

  • Stefan Lang

    (University of Innsbruck, Department of Statistics)

  • Brian Marx

    (Louisiana State University, Experimental Statistics)

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.

Suggested Citation

  • Ludwig Fahrmeir & Thomas Kneib & Stefan Lang & Brian Marx, 2013. "Generalized Linear Models," Springer Books, in: Regression, edition 127, chapter 5, pages 269-324, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-34333-9_5
    DOI: 10.1007/978-3-642-34333-9_5
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