A Proof of Asymptotic Normality for some VARX Models.
A propos du théorème de Cassels-Schmidt concernant de nombres normaux
A quenched weak invariance principle
In this paper we study the almost sure conditional central limit theorem in its functional form for a class of random variables satisfying a projective criterion. Applications to strongly mixing processes and nonirreducible Markov chains are given. The proofs are based on the normal approximation of double indexed martingale-like sequences, an approach which has interest in itself.
A refinement of normal approximation to Poisson binomial.
A regeneration proof of the central limit theorem for uniformly ergodic Markov chains.
A remark on the conditioning in limit theorems for dependent random vector in
A Remark on the Strong Law of Large Numbers.
A remark on Vapnik-Chervonienkis classes
We show that the family of all lines in the plane which is a VC class of index 2 cannot be obtained in a finite number of steps starting with VC classes of index 1 and applying the operations of intersection and union. This confirms a common belief among specialists and solves a question asked by several authors.
A Renewal Approach to the Perron-Frobenius Theory of Non-negative Kernels on General State Spaces.
A representation formula for large deviations rate functionals of invariant measures on the one dimensional torus
We consider a generic diffusion on the 1D torus and give a simple representation formula for the large deviation rate functional of its invariant probability measure, in the limit of vanishing noise. Previously, this rate functional had been characterized by M. I. Freidlin and A. D. Wentzell as solution of a rather complex optimization problem. We discuss this last problem in full generality and show that it leads to our formula. We express the rate functional by means of a geometric transformation...
A second order approximation for the inverse of the distribution function of the sample mean
The classical quantile approximation for the sample mean, based on the central limit theorem, has been proved to fail when the sample size is small and we approach the tail of the distribution. In this paper we will develop a second order approximation formula for the quantile which improves the classical one under heavy tails underlying distributions, and performs very accurately in the upper tail of the distribution even for relatively small samples.
A semigroup setting for distance measures in connexion with Poisson approximation.
A semigroup-theoretic proof of Poisson's limit law.
A semigroup-theoretic proof of the law of large numbers.
A sharp analysis on the asymptotic behavior of the Durbin–Watson statistic for the first-order autoregressive process
The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin–Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic normality for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated with the driven noise....
A Sharper Form of the Doeblin-Lévy-Kolmogorov-Rogozin Inequality for Concentration Functions.
A simple characterization of almost uniform convergence by stochastic convergence.
A singular large deviations phenomenon
A strong invariance principle for negatively associated random fields
In this paper we obtain a strong invariance principle for negatively associated random fields, under the assumptions that the field has a finite th moment and the covariance coefficient exponentially decreases to . The main tools are the Berkes-Morrow multi-parameter blocking technique and the Csörgő-Révész quantile transform method.
A strong invariance theorem for the tail empirical process