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Generalized Additive Models

  • Jaroslaw Harezlak
  • David Ruppert
  • Matt P. Wand
Chapter
Part of the Use R! book series (USE R)

Abstract

The models fit in Chap. 2 have two limitations. First, the conditional distribution of the response, given the predictors, is assumed to be Gaussian. Second, only a single predictor is allowed to have a smooth nonlinear effect—the other predictors are modeled linearly. The first limitation is addressed by using generalized linear models (GLMs), which remove the Gaussian assumption and allow the response variable to have other distributions such as those within the Binomial and Poisson families.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jaroslaw Harezlak
    • 1
  • David Ruppert
    • 2
  • Matt P. Wand
    • 3
  1. 1.School of Public HealthIndiana University BloomingtonBloomingtonUSA
  2. 2.Department of Statistical ScienceCornell UniversityIthacaUSA
  3. 3.School of Mathematical and Physical SciencesUniversity of Technology SydneyUltimoAustralia

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