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Theses

Statistique d’extrêmes de variables aléatoires fortement corrélées

Abstract : Extreme value statistics plays a keyrole in various scientific contexts. Although the description of the statistics of a global extremum is certainly an important feature, it focuses on the fluctuations of a single variable among many others. A natural question that arises is then the following: is this extreme value lonely at the top or, on the contrary, are there many other variables close to it ? A natural and useful quantity to characterize the crowding is the density of states near extremes. For this quantity, there exist very few exact results for strongly correlated variables, which is however the case encountered in many situations. Two physical models of strongly correlated variables have attracted much attention because they can be studied analytically : the positions of a random walker and the eigenvalues of a random matrix. This thesis is devoted to the study of the statistics near the maximum of these two ensembles of strongly correlated variables.In the first part, I study the case where the collection of random variables is the position of a Brownian motion, which may be constrained to be periodic or positive. This Brownian motion is seen as the limit of a classical random walker after a large number of steps. It is then possible to interpret this problem as a quantum particle in a potential which allows us to use powerful methods from quantum mechanics as propagators and path integral. These tools are used to calculate the average density from the maximum for different constrained Brownian motions and the complete distribution of this observable in certain cases. It is also possible to generalize this approach to the study of several random walks, independent or with interaction, as well as to the study of other functional of the maximum.In the second part, I study the case of the eigenvalues of random matrices, belonging to both Gaussian and Wishart ensembles. The study near the maximal eigenvalues for both models is performed using a method based on semi-classical orthogonal polynomials. In the case of Gaussian unitary matrices, I have obtained an analytical formula for the density near the maximum as well as a new expression for the distribution of the gap between the two largest eigenvalues. These results, and in particular their generalizations to different Gaussian ensembles, are then applied to the relaxational dynamics of a mean-field spin glass model. Finally, for the case of Wishart matrices I proposed a new derivation of the distribution of the smallest eigenvalue using orthogonal polynomials. In addition, I proved a conjecture on the first finite size correction of this distribution in the «hard edge» limit.
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Anthony Perret. Statistique d’extrêmes de variables aléatoires fortement corrélées. Physique mathématique [math-ph]. Université Paris Sud - Paris XI, 2015. Français. ⟨NNT : 2015PA112110⟩. ⟨tel-01171835⟩

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