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...
Density of paths of iterated Lévy transforms of brownian motion
The Lévy transform of a Brownian motion B is the Brownian motion B(1) given by Bt(1) = ∫0tsgn(Bs)dBs; call B(n) the Brownian motion obtained from B by iterating n times this transformation. We establish that almost surely, the sequence of paths (t → Bt(n))n⩾0 is dense in Wiener space, for the topology of uniform convergence on compact time intervals.
Density of paths of iterated Lévy transforms of Brownian motion
The Lévy transform of a Brownian motion B is the Brownian motion B(1) given by Bt(1) = ∫0tsgn(Bs)dBs; call B(n) the Brownian motion obtained from B by iterating n times this transformation. We establish that almost surely, the sequence of paths (t → Bt(n))n⩾0 is dense in Wiener space, for the topology of uniform convergence on compact time intervals.
Density of safe matrices.
Dépassement des sommes partielles de v.a.r. indépendantes équidistribuées sans moment d'ordre 1
Dependence Measuring from Conditional Variances
A conditional variance is an indicator of the level of independence between two random variables. We exploit this intuitive relationship and define a measure v which is almost a measure of mutual complete dependence. Unsurprisingly, the measure attains its minimum value for many pairs of non-independent ran- dom variables. Adjusting the measure so as to make it invariant under all Borel measurable injective trans- formations, we obtain a copula-based measure of dependence v* satisfying A. Rényi’s...
Dependent defaults and credit migrations
The paper deals with the modelling of mutually dependent default times of several credit names through the intensity-based approach. We extend to the case of multiple ratings some previous results due to Schmidt (1998), Kusuoka (1999) and Jarrow and Yu (2001). The issue of the arbitrage valuation of simple basket credit derivatives is also briefly examined. We argue that our approach leads, in some cases, to a significant reduction of the dimensionality of the valuation problem at hand.
Dependent Lindeberg central limit theorem and some applications
In this paper, a very useful lemma (in two versions) is proved: it simplifies notably the essential step to establish a Lindeberg central limit theorem for dependent processes. Then, applying this lemma to weakly dependent processes introduced in Doukhan and Louhichi (1999), a new central limit theorem is obtained for sample mean or kernel density estimator. Moreover, by using the subsampling, extensions under weaker assumptions of these central limit theorems are provided. All the usual causal...
Dependent random variables with a given marginal distribution
Depinning of a polymer in a multi-interface medium.
Der diskontierte Einarmige Bandit.
Der Satz von Choquet-Bishop-de Leeuw für konvexe nicht kompakte Mengen straffer Maße.