A unified mixture model based on the inverse gaussian distribution

Victor Leiva, Antonio Sanhueza, Samuel Kotz, Nelson Araneda

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

In this paper, we introduce a new class of mixture models based on the inverse Gaussian distribution, which is highly flexible and contains several well-known probability models. The new class of models is generated from symmetric distributions around zero by using the connection between the inverse Gaussian and standard normal distributions. We illustrate the obtained results by means of two real data sets through likelihood, goodness-of-fit and diagnostic methods. This illustration indicates the adequacy of the new model.

Original languageEnglish
Pages (from-to)445-460
Number of pages16
JournalPakistan Journal of Statistics
Volume26
Issue number3
StatePublished - Jul 2010
Externally publishedYes

Keywords

  • Birnbaum-saunders distribution
  • Elliptic distributions
  • Kurtosis
  • Length-biased distributions
  • Mixture distributions
  • Moments
  • Robustness
  • Skewness

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