Chapter 11: Positive Continuous Data: Gamma and Inverse Gaussian GLMs

  • Peter K. Dunn
  • Gordon K. Smyth
Part of the Springer Texts in Statistics book series (STS)


This chapter considers models for positive continuous data. Variables that take positive and continuous values often measure the amount of some physical quantity that is always present. The two most common glms for this type of data are based on the gamma and inverse Gaussian distributions. Judicious choice of link function and transformations of the covariates ensure that a variety of relationships between the response and explanatory variables can be modelled. Modelling positive continuous data is introduced in Sect. 11.2, then the two most common edms for modelling positive continuous data are discussed: gamma distributions (Sect. 11.3) and inverse Gaussian distributions (Sect. 11.4). The use of link functions is then addressed (Sect. 11.5). Finally, estimation of ϕ is considered in Sect. 11.6.


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

Authors and Affiliations

  • Peter K. Dunn
    • 1
  • Gordon K. Smyth
    • 2
  1. 1.Faculty of Science, Health, Education and EngineeringSchool of Health of Sport Science, University of the Sunshine CoastQueenslandAustralia
  2. 2.Bioinformatics DivisionWalter and Eliza Hall Institute of Medical ResearchParkvilleAustralia

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