Kummer and gamma laws through independences on trees - another parallel with the Matsumoto-Yor property
Agnieszka Piliszek, Jacek Weso{\l}owski

TL;DR
This paper explores a novel multivariate distribution on trees called the tree-Kummer distribution, demonstrating that certain transformations induce independence among components and characterizing the laws through these properties.
Contribution
It introduces the multivariate tree-Kummer distribution, establishes independence properties under specific transformations, and characterizes gamma and Kummer laws via these properties.
Findings
Directed trees induce independence in the tree-Kummer distribution.
Transformations reveal gamma and Kummer distributions through independence.
Characterization of laws based on independence properties.
Abstract
The paper develops a rather unexpected parallel to the multivariate Matsumoto--Yor (MY) property on trees considered in \cite{MW04}. The parallel concerns a multivariate version of the Kummer distribution, which is generated by a tree. Given a tree of size , we direct it by choosing a vertex, say , as a root. With such a directed tree we associate a map . For a random vector having a -variate tree-Kummer distribution and any root , we prove that has independent components. Moreover, we show that if is a random vector in and for any leaf of the tree the components of are independent, then one of these components has a Gamma distribution and the remaining components have Kummer distributions. Our point of departure is a relatively simple independence property due to \cite{HV15}. It states…
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