The Matsumoto-Yor property on trees
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Publication:1763109
DOI10.3150/bj/1093265636zbMath1055.62012OpenAlexW2003861318MaRDI QIDQ1763109
Jacek Wesołowski, Hélène Massam
Publication date: 21 February 2005
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3150/bj/1093265636
treegammaindependenceMatsumoto-Yor propertyinverse GaussianWishartcharacterization of the product of a gamma and generalized inverse Gaussian
Characterization and structure theory for multivariate probability distributions; copulas (62H05) Probability distributions: general theory (60E05) Characterization and structure theory of statistical distributions (62E10)
Related Items (12)
Kummer and gamma laws through independences on trees -- another parallel with the Matsumoto-Yor property ⋮ Hitting times of Brownian motion and the Matsumoto-Yor property on trees ⋮ Multivariate Matsumoto-Yor property is rather restrictive ⋮ Change of measure technique in characterizations of the gamma and Kummer distributions ⋮ Unnamed Item ⋮ On the Matsumoto-Yor type regression characterization of the gamma and Kummer distributions ⋮ A link between the Matsumoto-Yor property and an independence property on trees ⋮ Characterizations of GIG laws: a survey ⋮ Tree structured independence for exponential Brownian functionals ⋮ Multivariate reciprocal inverse Gaussian distributions from the Sabot-Tarrès-Zeng integral ⋮ The Matsumoto-Yor property on trees for matrix variates of different dimensions ⋮ The Matsumoto\,-\,Yor property and the structure of the Wishart distribution
Cites Work
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- Interpretation via Brownian motion of some independence properties between GIG and gamma variables.
- An independence property for the product of GIG and gamma laws
- An Analogue of Pitman’s 2M — X Theorem for Exponential Wiener Functionals Part II: The Role of the Generalized Inverse Gaussian Laws
- The Matsumoto–Yor independence property for GIG and Gamma laws, revisited
- A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
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