Das Gesetz des iterierten Logarithmus für kumulative Prozesse.
Das Gesetz vom iterierten Logarithmus für empirische Verteilungsfunktionen im ... .
Das Gesetz vom iterierten Logarithmus mit Anwendungen auf die Zahlentheorie.
Davis-type theorems for martingale difference sequences.
Decay of correlations in one-dimensional dynamics
Degenerate limit laws for the maxima of trigonometric polynomials with random coefficients
Densité en temps petit d'un processus de saut
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.
Dépassement des sommes partielles de v.a.r. indépendantes équidistribuées sans moment d'ordre 1
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...
Depinning of a polymer in a multi-interface medium.
Dérivabilité du plus grand exposant caractéristique des produits de matrices aléatoires indépendantes à coefficients positifs
Determinantal processes and independence.
Determinism and martingale decompositions of strictly stationary random processes
Deviation bounds for additive functionals of Markov processes
In this paper we derive non asymptotic deviation bounds forwhere is a stationary and ergodic Markov process and is some integrable function. These bounds are obtained under various moments assumptions for , and various regularity assumptions for . Regularity means here that may satisfy various functional inequalities (F-Sobolev, generalized Poincaré etc.).
deviation bounds for additive functionals of markov processes
In this paper we derive non asymptotic deviation bounds for where X is a μ stationary and ergodic Markov process and V is some μ integrable function. These bounds are obtained under various moments assumptions for V, and various regularity assumptions for μ. Regularity means here that μ may satisfy various functional inequalities (F-Sobolev, generalized Poincaré etc.).
Deviation inequalities and moderate deviations for estimators of parameters in an Ornstein-Uhlenbeck process with linear drift.
Deviation inequalities and moderate deviations for estimators of parameters in bifurcating autoregressive models
The purpose of this paper is to investigate the deviation inequalities and the moderate deviation principle of the least squares estimators of the unknown parameters of general th-order asymmetric bifurcating autoregressive processes, under suitable assumptions on the driven noise of the process. Our investigation relies on the moderate deviation principle for martingales.
Dichotomy in a scaling limit under Wiener measure with density.