Classes of measures closed under mixing and convolution. Weak stability

Jolanta K. Misiewicz ; Krzysztof Oleszkiewicz ; Kazimierz Urbanik

Studia Mathematica, Tome 166 (2005), p. 195-213 / Harvested from The Polish Digital Mathematics Library

Résumé

For a random vector X with a fixed distribution μ we construct a class of distributions ℳ(μ) = μ∘λ: λ ∈ , which is the class of all distributions of random vectors XΘ, where Θ is independent of X and has distribution λ. The problem is to characterize the distributions μ for which ℳ(μ) is closed under convolution. This is equivalent to the characterization of the random vectors X such that for all random variables Θ₁, Θ₂ independent of X, X’ there exists a random variable Θ independent of X such that $XΘ₁ + X^{\prime }Θ₂ \stackrel{d}{=} XΘ$. We show that for every X this property is equivalent to the following condition: ∀ a,b ∈ ℝ ∃ Θ independent of X, $aX + bX^{\prime } \stackrel{d}{=} XΘ$. This condition reminds the characterizing condition for symmetric stable random vectors, except that Θ is here a random variable, instead of a constant. The above problem has a direct connection with the concept of generalized convolutions and with the characterization of the extreme points for the set of pseudo-isotropic distributions.

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Publié le : 2005-01-01
EUDML-ID : urn:eudml:doc:284446