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GLM and GAM for Count Data

  • Alain F. Zuur
  • Elena N. Ieno
  • Neil J. Walker
  • Anatoly A. Saveliev
  • Graham M. Smith
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

A generalised linear model (GLM) or a generalised additive model (GAM) consists of three steps: (i) the distribution of the response variable, (ii) the specification of the systematic component in terms of explanatory variables, and (iii) the link between the mean of the response variable and the systematic part. In Chapter 8, we discussed several different distributions for the response variable: Normal, Poisson, negative binomial, geometric, gamma, Bernoulli, and binomial distributions. One of these distributions can be used for the first step mentioned above. In fact, later in Chapter 11, we see how you can also use a mixture of two distributions for the response variable; but in this chapter, we only work with one distribution at a time.

Keywords

Explanatory Variable Generalise Linear Model Generalise Additive Model Dispersion Parameter Negative Binomial Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Alain F. Zuur
    • 1
  • Elena N. Ieno
    • 1
  • Neil J. Walker
    • 2
  • Anatoly A. Saveliev
    • 3
  • Graham M. Smith
    • 4
  1. 1.Highland Statistics LtdNewburghUK
  2. 2.Central Science LaboratoryGloucesterUK
  3. 3.Kazan State UniversityKazanRussia
  4. 4.Bath Spa UniversityBathUK

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