Statistics and Computing

, Volume 15, Issue 4, pp 267–280 | Cite as

Series evaluation of Tweedie exponential dispersion model densities

  • Peter K. DunnEmail author
  • Gordon K. Smyth


Exponential dispersion models, which are linear exponential families with a dispersion parameter, are the prototype response distributions for generalized linear models. The Tweedie family comprises those exponential dispersion models with power mean-variance relationships. The normal, Poisson, gamma and inverse Gaussian distributions belong to theTweedie family. Apart from these special cases, Tweedie distributions do not have density functions which can be written in closed form. Instead, the densities can be represented as infinite summations derived from series expansions. This article describes how the series expansions can be summed in an numerically efficient fashion. The usefulness of the approach is demonstrated, but full machine accuracy is shown not to be obtainable using the series expansion method for all parameter values. Derivatives of the density with respect to the dispersion parameter are also derived to facilitate maximum likelihood estimation. The methods are demonstrated on two data examples and compared with with Box-Cox transformations and extended quasi-likelihoood.


linear exponential family generalized linear models power variance function Poisson distribution gamma distribution inverse-Gaussian distribution compound Poisson distributions stable distributions maximum likelihood estimation 


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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Department of Mathematics and ComputingUniversity of Southern QueenslandToowoombaAustralia
  2. 2.Division of Genetics and BioinformaticsWalter and Eliza Hall Institute of Medical ResearchMelbourneAustralia

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