La convergence en probabilité et la convergence presque sûre de certaines suites de variables aléatoires dépendantes
La convergence presque sûre des -moyennes de Césaro
La démonstration du second théorème limite du calcul de probabilité par la méthode de Cauchy-Lévy reposant sur la fonction caractéristique
La loi des grande nombres pour les marches aléatoires sur le dual de SU(2)
La loi des grands nombres de Prokhorov dans les espaces de type
La loi des grands nombres pour une suite échangeable
La loi du logarithme itéré pour les variables aléatoires prégaussiennes à valeurs dans un espace de Banach à norme régulière
La loi forte des grands nombres pour les processus harmonisables
LAMN property for hidden processes : the case of integrated diffusions
In this paper we prove the Local Asymptotic Mixed Normality (LAMN) property for the statistical model given by the observation of local means of a diffusion process X. Our data are given by ∫01X(s+i)/n dμ(s) for i=0, …, n−1 and the unknown parameter appears in the diffusion coefficient of the process X only. Although the data are neither markovian nor gaussian we can write down, with help of Malliavin calculus, an explicit expression for the log-likelihood of the model, and then study the asymptotic...
Laplace transform estimates and deviation inequalities
Large and moderate deviations for Hotelling's -statistic.
Large and moderate deviations for moving average processes
Large and moderate deviations for the local time of a recurrent Markov chain on
Large and Moderate Deviations Principles for Recursive Kernel Estimator of a Multivariate Density and its Partial Derivatives
2000 Mathematics Subject Classification: 62G07, 60F10.In this paper we prove large and moderate deviations principles for the recursive kernel estimator of a probability density function and its partial derivatives. Unlike the density estimator, the derivatives estimators exhibit a quadratic behaviour not only for the moderate deviations scale but also for the large deviations one. We provide results both for the pointwise and the uniform deviations.
Large deviation estimate of transition densities for jump processes
Large deviation principle and inviscid shell models.
Large deviation principle for empirical measures generated by Cox point processes
Large deviation principle for enhanced gaussian processes
Large deviation principle of occupation measure for stochastic Burgers equation
Large deviation principles and generalized Sherrington-Kirkpatrick models