Corrections : «Théorèmes limites pour les temps locaux d'un processus stable symétrique»
Corrigendum on "Semigroup-theoretical proofs of the central limit theorem and other theorems of analysis.
Corrigendum to “Stability of solutions of BSDEs with random terminal time”
This paper is a corrigendum to paper Toldo, ESAIM, P&S10 (2006) 141–163 where we study the stability of the solutions of Backward Stochastic Differential Equations (BSDE for short) with an almost surely finite random terminal time.
Counting occurrences in almost sure limit theorems
Covariance inequalities for strongly mixing processes
Cramer Type Large Deviations for Studentized U-Statistics.
Cramér type moderate deviations for Studentized U-statistics
Let Tn be a Studentized U-statistic. It is proved that a Cramér type moderate deviation P(Tn ≥ x)/(1 − Φ(x)) → 1 holds uniformly in x ∈ [0, o(n1/6)) when the kernel satisfies some regular conditions.
Cramér type moderate deviations for Studentized U-statistics******
Let Tn be a Studentized U-statistic. It is proved that a Cramér type moderate deviation P(Tn ≥ x)/(1 − Φ(x)) → 1 holds uniformly in x∈ [0, o(n1/6)) when the kernel satisfies some regular conditions.
Critical branching diffusions : proper normalization and conditioned limit
Decay of correlations in one-dimensional dynamics
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.
Determinantal processes and independence.
Determinism and martingale decompositions of strictly stationary random processes
Diffusion approximations of the geometric Markov renewal processes and option price formulas.
Discrete limit theorems for the Laplace transform of the Riemann zeta-function
In the paper discrete limit theorems in the sense of weak convergence of probability measures on the complex plane as well as in the space of analytic functions for the Laplace transform of the Riemann zeta-function are proved.
Distribuciones binomiales y de Bernoulli de probabilidad aleatoria. Teoremas del límite central.