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Journal of Theoretical Probability

, Volume 24, Issue 3, pp 746–755 | Cite as

On the Uniqueness of the Kendall Generalized Convolution

Article

Abstract

Kendall (Foundations of a theory of random sets, in Harding, E.F., Kendall, D.G. (eds.), pp. 322–376, Willey, New York, 1974) showed that the operation \(\diamond_{1}\colon \mathcal{P}_{+}^{2}\rightarrow \mathcal{P}_{+}\) given by
$$\delta_x\diamond_1\delta_1=x\pi_2+(1-x)\delta_1,$$
where x∈[0,1] and π β is the Pareto distribution with the density function β s β−1 on the set [1,∞), defines a generalized convolution on ℘+. Kucharczak and Urbanik (Quasi-stable functions, Bull. Pol. Acad. Sci., Math. 22(3):263–268, 1974) noticed that also the following operation
$$\delta_x\diamond_{\alpha}\delta_1=x^{\alpha}\pi_{2\alpha}+\bigl(1-x^{\alpha}\bigr)\delta_1$$
defines generalized convolutions on ℘+. In this paper, we show that α convolutions are the only possible convolutions defined by the convex linear combination of two fixed measures. To be precise, we show that if :2→℘ is a generalized convolution defined by
$$\delta_x\diamond \delta_1=p(x)\lambda_1+\bigl(1-p(x)\bigr)\lambda_2,$$
for some fixed probability measures λ 1,λ 2 and some continuous function p :[0,1]→[0,1], p(0)=0=1−p(1), then there exists an α>0 such that p(x)=x α , = α , λ 1=π 2α and λ 2=δ 1. We present a similar result also for the corresponding weak generalized convolution.

Keywords

Weakly stable distribution Generalized weak convolution Generalized convolution Pareto distribution 

Mathematics Subject Classification (2000)

60A10 60B05 60E05 60E07 60E10 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Institute of MathematicsUniversity of WrocławWrocławPoland
  2. 2.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland

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