, Volume 68, Issue 1, pp 31–49 | Cite as

A new class of inverse Gaussian type distributions

  • Antonio Sanhueza
  • Víctor Leiva
  • N. Balakrishnan


The elliptical laws are a class of symmetrical probability models that include both lighter and heavier tailed distributions. These models may adapt well to the data, even when outliers exist and have other good theoretical properties and application perspectives. In this article, we present a new class of models, which is generated from symmetrical distributions in \({\mathbb{R}}\) and generalize the well known inverse Gaussian distribution. Specifically, the density, distribution function, properties, transformations and moments of this new model are obtained. Also, a graphical analysis of the density is provided. Furthermore, we estimate parameters, propose asymptotic inference and discuss influence diagnostics by using likelihood methods for the new distribution. In particular, we show that the maximum likelihood estimates parameters of the new model under the t kernel are down-weighted for the outliers. Thus, smaller weights are attributed to outlying observations, which produce robust parameter estimates. Finally, an illustrative example with real data shows that the new distribution fits better to the data than some other well known probabilistic models.


Elliptical distributions Diagnostics Kurtosis Likelihood methods Moments 


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Antonio Sanhueza
    • 1
  • Víctor Leiva
    • 2
  • N. Balakrishnan
    • 3
  1. 1.Department of Mathematics and StatisticsUniversidad de La FronteraTemucoChile
  2. 2.Department of StatisticsUniversidad de ValparaísoValparaísoChile
  3. 3.Department of Mathematics and StatisticsMcMaster UniversityHamiltonCanada

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