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Conference papers

Saddlepoint Approximations of Cumulative Distribution Functions of Sums of Random Vectors

Dadja Anade 1 Jean-Marie Gorce 1 Philippe Mary 2 Samir Perlaza 3
1 MARACAS - Modèle et algorithmes pour des systèmes de communication fiables
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
3 NEO - Network Engineering and Operations
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In this paper, a real-valued function that approximates the cumulative distribution function (CDF) of a finite sum of real-valued independent and identically distributed random vectors is presented. The approximation error is upper bounded by an expression that is easy to calculate. As a byproduct, an upper bound and a lower bound on the CDF are obtained. Finally, in the case of lattice and absolutely continuous random variables, the proposed approximation is shown to be identical to the saddlepoint approximation of the CDF.
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Submitted on : Thursday, May 13, 2021 - 8:18:15 PM
Last modification on : Monday, May 16, 2022 - 4:54:01 PM
Long-term archiving on: : Saturday, August 14, 2021 - 6:16:18 PM


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Dadja Anade, Jean-Marie Gorce, Philippe Mary, Samir Perlaza. Saddlepoint Approximations of Cumulative Distribution Functions of Sums of Random Vectors. ISIT 2021 - IEEE International Symposium on Information Theory, Jul 2021, Melbourne / Virtual, Australia. pp.1-6, ⟨10.1109/ISIT45174.2021.9518101⟩. ⟨hal-03226009⟩



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