Extending the multivariate generalised t and generalised V G distributions

Thomas Fung, Eugene Seneta*

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

The G G H family of multivariate distributions is obtained by scale mixing on the Exponential Power distribution using the Extended Generalised Inverse Gaussian distribution. The resulting G G H family encompasses the multivariate generalised hyperbolic (G H), which itself contains the multivariate t and multivariate Variance-Gamma (V G) distributions as special cases. It also contains the generalised multivariate t distribution [O. Arslan, Family of multivariate generalised t distribution, Journal of Multivariate Analysis 89 (2004) 329-337] and a new generalisation of the V G as special cases. Our approach unifies into a single G H-type family the hitherto separately treated t-type [O. Arslan, A new class of multivariate distribution: Scale mixture of Kotz-type distributions, Statistics and Probability Letters 75 (2005) 18-28; O. Arslan, Variance-mean mixture of Kotz-type distributions, Communications in Statistics-Theory and Methods 38 (2009) 272-284] and V G-type cases. The G G H distribution is dual to the distribution obtained by analogous mixing on the scale parameter of a spherically symmetric stable distribution. Duality between the multivariate t and multivariate V G [S.W. Harrar, E. Seneta, A.K. Gupta, Duality between matrix variate t and matrix variate V.G. distributions, Journal of Multivariate Analysis 97 (2006) 1467-1475] does however extend in one sense to their generalisations.

Original languageEnglish
Pages (from-to)154-164
Number of pages11
JournalJournal of Multivariate Analysis
Volume101
Issue number1
DOIs
Publication statusPublished - Jan 2010
Externally publishedYes

Keywords

  • Duality
  • Exponential Power distribution
  • Generalised Hyperbolic distribution
  • Generalised t distribution
  • Variance-gamma distribution

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