Demiconvergence of processes indexed by two indices
Démonstration «atomique» des inégalités de Burkholder-Davis-Gundy
Démonstration d'un théorème de F. Knight à l'aide de martingales exponentielles
Démonstration élémentaire d'un résultat d'Azéma et Jeulin
Démonstration élémentaire d'un théorème de Novikov
Démonstration élémentaire d'une proposition générale de la théorie des probabilités.
Démonstration probabiliste du théorème de d'Alembert
Démonstration probabiliste d'une identité de convolution
Démonstration simple d'un résultat sur le temps local
Densité des diffusions en temps petit : développements asymptotiques (part I)
Densité des orbites des trajectoires browniennes sous l’action de la transformation de Lévy
Let Tbe a measurable transformation of a probability space , preserving the measureπ. Let X be a random variable with law π. Call K(⋅, ⋅) a regular version of the conditional law of X given T(X). Fix . We first prove that ifB is reachable from π-almost every point for a Markov chain of kernel K, then the T-orbit of π-almost every point X visits B. We then apply this result to the Lévy transform, which transforms the Brownian motion W into the Brownian motion |W| − L, where L is the local time...
Densité en temps petit d'un processus de saut
Density estimates on a parabolic SPDE.
Density estimation for one-dimensional dynamical systems
In this paper we prove a Central Limit Theorem for standard kernel estimates of the invariant density of one-dimensional dynamical systems. The two main steps of the proof of this theorem are the following: the study of rate of convergence for the variance of the estimator and a variation on the Lindeberg–Rio method. We also give an extension in the case of weakly dependent sequences in a sense introduced by Doukhan and Louhichi.
Density Estimation for One-Dimensional Dynamical Systems
In this paper we prove a Central Limit Theorem for standard kernel estimates of the invariant density of one-dimensional dynamical systems. The two main steps of the proof of this theorem are the following: the study of rate of convergence for the variance of the estimator and a variation on the Lindeberg–Rio method. We also give an extension in the case of weakly dependent sequences in a sense introduced by Doukhan and Louhichi.
Density estimation via best -approximation on classes of step functions
We establish consistent estimators of jump positions and jump altitudes of a multi-level step function that is the best -approximation of a probability density function . If itself is a step-function the number of jumps may be unknown.
Density fluctuations for a zero-range process on the percolation cluster.
Density formula and concentration inequalities with Malliavin calculus.
Density in small time for Levy processes
Density in small time for Lévy processes
The density of real-valued Lévy processes is studied in small time under the assumption that the process has many small jumps. We prove that the real line can be divided into three subsets on which the density is smaller and smaller: the set of points that the process can reach with a finite number of jumps (Δ-accessible points); the set of points that the process can reach with an infinite number of jumps (asymptotically Δ-accessible points); and the set of points that the process cannot...