Add to ORKGIn this paper we extend a theorem of Bradley under interlaced mixing and strong mixing conditions. More precisely, we study the asymptotic normality of the normalized partial sum of an α-mixing strictly stationary random field of random vectors, in the presence of another dependence assumption.2000 AMS Mathematics Subject Classification: Primary: 60F05; Secondary: 60G60.In this paper we extend a theorem of Bradley under interlaced mixing and strong mixing conditions. More precisely, we study the asymptotic normality of the normalized partial sum of an α-mixing strictly stationary random field of random vectors, in the presence of another dependence assumption.2000 AMS Mathematics Subject Classification: Primary: 60F05; Secondary...
We prove the asymptotic normality of the kernel density estimator (introduced by Rosenblatt (1956) a...
We prove a self normalized central limit theorem for a new mixing class of processes introduced in K...
For set-indexed partial sums processes of stationary mixing random fields, convergence of finite dim...
A central limit theorem is proved for α-mixing random fields. The sets of locations where the random...
AbstractThis article is motivated by a central limit theorem of Ibragimov for strictly stationary ra...
We prove an almost sure central limit theorem (ASCLT) for strongly mixing sequence of random variabl...
This paper shows how the modern machinery for generating abstract empirical central limit theorems c...
We prove an almost sure central limit theorem (ASCLT) for strongly mixing sequence of random variabl...
A Central Limit Theorem is proved for linear random fields when sums are taken over union of finitel...
AbstractA distributional mixing condition is introduced for stationary sequences of random vectors t...
Abstract. For a given pair of positive integers d and N with N at least 2, for strictly stationary r...
The aim of this paper is to give a functional form for the central limit theorem obtained by Bradley...
This paper establishes a central limit theorem (CLT) for empirical processes indexed by smooth funct...
International audienceWe prove a central limit theorem for stationary multiple (random) fields of ma...
AbstractThis paper establishes a central limit theorem and an invariance principle for a wide class ...
We prove the asymptotic normality of the kernel density estimator (introduced by Rosenblatt (1956) a...
We prove a self normalized central limit theorem for a new mixing class of processes introduced in K...
For set-indexed partial sums processes of stationary mixing random fields, convergence of finite dim...
A central limit theorem is proved for α-mixing random fields. The sets of locations where the random...
AbstractThis article is motivated by a central limit theorem of Ibragimov for strictly stationary ra...
We prove an almost sure central limit theorem (ASCLT) for strongly mixing sequence of random variabl...
This paper shows how the modern machinery for generating abstract empirical central limit theorems c...
We prove an almost sure central limit theorem (ASCLT) for strongly mixing sequence of random variabl...
A Central Limit Theorem is proved for linear random fields when sums are taken over union of finitel...
AbstractA distributional mixing condition is introduced for stationary sequences of random vectors t...
Abstract. For a given pair of positive integers d and N with N at least 2, for strictly stationary r...
The aim of this paper is to give a functional form for the central limit theorem obtained by Bradley...
This paper establishes a central limit theorem (CLT) for empirical processes indexed by smooth funct...
International audienceWe prove a central limit theorem for stationary multiple (random) fields of ma...
AbstractThis paper establishes a central limit theorem and an invariance principle for a wide class ...
We prove the asymptotic normality of the kernel density estimator (introduced by Rosenblatt (1956) a...
We prove a self normalized central limit theorem for a new mixing class of processes introduced in K...
For set-indexed partial sums processes of stationary mixing random fields, convergence of finite dim...