Thèse soutenue

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Auteur / Autrice : Salim Noreddine
Direction : Ivan Nourdin
Type : Thèse de doctorat
Discipline(s) : Mathématiques
Date : Soutenance en 2013
Etablissement(s) : Paris 6

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Résumé

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This dissertation provides new insights into central limit theorems for multiple stochastic integrals and random matrices. The cornerstone of this work is the central limit theorem by Nualart and Peccati (called the Fourth Moment Theorem in the sequel), stating that a necessary and sufficient condition for a sequence of unit-variance random variables belonging to a given Wiener chaos to converge to a standard normal law is that its fourth moment converges to 3. The first chapter contain two new results. First, we give an explicit upper bound for the distance between a vector of multiple stochastic integrals F and a Gaussian vector, as a function of the fourth-order cumulants of the components of F. Second, we generalize the explicit formula for cumulants of Nourdin and Peccati to the vectorial case and we use it to offer a new proof for the vectorial version, originally due to Peccati and Tudor of the Fourth Moment Theorem. The second chapter is devoted to proving a central limit theorem for complex random matrices with independent identically distributed entries admitting moments of any order. This result is a generalization of a theorem by Nourdin and Peccati , which was only focused on real random matrices. Finally, in the Third chapter one considers a free probability environment and establish an equivalent of the Fourth Moment Theorem by Nualart and Peccati for q-Gaussians. We consider possible extensions to the q-Gaussian case of some of the afore-mentioned results obtained in the Gaussian case. Note furthermore that Kemp, Nourdin, Peccati and Speicher have established a Fourth Moment Theorem for the free Brownian motion. Since the latter is a particular q-Brownian motion, this chapter may be seen as a generalization of the paper of these four authors.