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Frangos, N. and Karlis, D. (2004) Modelling Losses Using an Exponential-Inverse Gaussian Distribution. Insurance: Mathematics and Economics, 35, 53-67.
https://doi.org/10.1016/j.insmatheco.2004.04.005

has been cited by the following article:

• TITLE: Gamma-Generalized Inverse Gaussian Class of Distributions

  AUTHORS: Richard L. K. Tinega, Joash M. Kerongo, Joseph A. M. Ottieno

  KEYWORDS: GIG, Mixture, Special Cases, Limiting Cases, Gamma, Mixing Distribution

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.6, December 23, 2021

  ABSTRACT: Gamma distribution nests exponential, chi-squared and Erlang distributions; while generalized Inverse Gaussian distribution nests quite a number of distributions. The aim of this paper is to construct a gamma mixture using Generalized inverse Gaussian mixing distribution. The rth moment of the mixture is obtained via the rth moment of the mixing distribution. Special cases and limiting cases of the mixture are deduced.