Dans cet exposé, on s'intéressera au spectre de grandes matrices aléatoires symétriques dont les coefficients sont i.i.d. centrés et réduits. Après avoir motivé l'étude de telles matrices, j'énoncerai un théorème de convergence des valeurs propres dû à Eugène Wigner. La

Les matrices de Wigner sont de grandes matrices aléatoires Hermitiennes à coefficients indépendants identiquement distribués, dont le spectre est désormais bien compris. Dans cet exposé, nous nous intéresserons aux fluctuations du spectre de matrices de Wigner déformées de la forme

Random matrices appear in various theoretical and practical fields, from quantum physics to applied finance. The spectrum of random matrices plays an important role in such areas, for instance by quantifying random energy levels or estimating covariance matrices. In the

In this talk we study the cover time, i.e. the duration to visit every site of a finite graph. In 2012, Ding et al. published an inspiring result that the asymptotics of the cover time can be estimated by the

We are interested in the maximum of supercritical branching random walk or branching Brownian motion in the real line, which under some mild condition, shifted by $a*n-b\log n$, converges in law to some non-degenerate distribution. We first study the lower

Résumé à venir

Relaxing the stationarity assumption in time series analysis is a challenging problem. We will present an approach based on Markov chain techniques through a notion called local stationarity and which consists in replacing the usual asymptotic by an "infill" one. The main

Lipschitz-Killing (LK) curvatures are geometrical tools which permit to analyze d dimensional objects. Considering a black and white image in dimension d= 2, there are three LK curvatures: the surface area, the half perimeter and the Euler characteristic. Each of

Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Any decision about releasing data  is supported by the estimation of measures of disclosure risk. The most common measure is arguably the number

In this talk, I will first introduce the post-model-selection inference setting, that has recently been subject to intensive investigation. In the case of Gaussian linear regression, I will review the post-model-selection confidence intervals suggested by Berk et al (2013). These