Add to ORKGThis paper shows how the modern machinery for generating abstract empirical central limit theorems can be applied to arrays of dependent variables. It develops a bracketing approximation based on a moment inequality for sums of strong mixing arrays, in an effort to illustrate the sorts of difficulty that need to be overcome when adapting the empirical process theory for independent variables. Some suggestions for further development are offered. The paper is largely self-contained
This paper presents central limit theorems for triangular arrays of mixingale and near-epoch-depende...
A classical limit theorem of stochastic process theory concerns the sample cumulative distribution f...
Building on work of McLeish, we present a number of invariance principles for doubly indexed arrays ...
This paper shows how the modern machinery for generating abstract empirical central limit theorems c...
This paper establishes a central limit theorem (CLT) for empirical processes indexed by smooth funct...
AbstractThis paper establishes a central limit theorem (CLT) for empirical processes indexed by smoo...
AbstractThis article is motivated by a central limit theorem of Ibragimov for strictly stationary ra...
Pre-print; version dated May 1999This paper gives new conditions for the functional central limit th...
In this paper we extend a theorem of Bradley under interlaced mixing and strong mixing conditions. M...
AbstractWe establish a multivariate empirical process central limit theorem for stationary Rd-valued...
We prove a self normalized central limit theorem for a new mixing class of processes introduced in K...
We prove an almost sure central limit theorem (ASCLT) for strongly mixing sequence of random variabl...
International audienceWe establish a multivariate empirical process central limit theorem for statio...
In [6], Serfozo introduced a class of stochastic processes which he called semi-stationary processes...
The object of this paper is to prove the functional central limit theorem for the stationary process...
This paper presents central limit theorems for triangular arrays of mixingale and near-epoch-depende...
A classical limit theorem of stochastic process theory concerns the sample cumulative distribution f...
Building on work of McLeish, we present a number of invariance principles for doubly indexed arrays ...
This paper shows how the modern machinery for generating abstract empirical central limit theorems c...
This paper establishes a central limit theorem (CLT) for empirical processes indexed by smooth funct...
AbstractThis paper establishes a central limit theorem (CLT) for empirical processes indexed by smoo...
AbstractThis article is motivated by a central limit theorem of Ibragimov for strictly stationary ra...
Pre-print; version dated May 1999This paper gives new conditions for the functional central limit th...
In this paper we extend a theorem of Bradley under interlaced mixing and strong mixing conditions. M...
AbstractWe establish a multivariate empirical process central limit theorem for stationary Rd-valued...
We prove a self normalized central limit theorem for a new mixing class of processes introduced in K...
We prove an almost sure central limit theorem (ASCLT) for strongly mixing sequence of random variabl...
International audienceWe establish a multivariate empirical process central limit theorem for statio...
In [6], Serfozo introduced a class of stochastic processes which he called semi-stationary processes...
The object of this paper is to prove the functional central limit theorem for the stationary process...
This paper presents central limit theorems for triangular arrays of mixingale and near-epoch-depende...
A classical limit theorem of stochastic process theory concerns the sample cumulative distribution f...
Building on work of McLeish, we present a number of invariance principles for doubly indexed arrays ...