AbstractWe discuss three forms of convergence in distribution which are stronger than the normal weak convergence, and are non-topological in nature. We give Storokhod representation results for two of these modes of convergence, and give applications to sufficient statistics and conditioned Markov processes, which are more difficult to obtain using weak convergence
The weak convergence of the empirical process of strong mixing or associated random variables is stu...
none3noGiven a sequence (mn) of random probability measures on a metric space S, consider the condit...
Skorohod has shown that the convergence of sums of i.i.d. random variables to an a-stable Levy motio...
We discuss three forms of convergence in distribution which are stronger than the normal weak conver...
We discuss three forms of convergence in distribution which are stronger than the normal weak conver...
The purpose of this course was to present results on weak convergence and invariance principle with ...
AbstractWeak convergence of probability measures on function spaces has been active area of research...
We know that almost sure convergence of random variables implies their convergence in distribution. ...
Summary. Subspaces Da, a> 0, of D[0, 1] are defined and given cor~p!ete metrics d~ which are stro...
We know that almost sure convergence of random variables implies their convergence in distribution. ...
Subspaces D α , α > 0, of D [0, 1] are defined and given complete metrics d α which are stronger tha...
AbstractEarlier results on weak convergence to diffusion processes [8] are generalized to cases wher...
We consider the weak convergence of numerical methods for stochastic differential equations (SDEs). ...
The hypo-convergence of upper semicontinuous functions provides a natural framework for the study of...
Let {X(n), n greater-than-or-equal-to 1} be a sequence of independent random variables and M(n) = ma...
The weak convergence of the empirical process of strong mixing or associated random variables is stu...
none3noGiven a sequence (mn) of random probability measures on a metric space S, consider the condit...
Skorohod has shown that the convergence of sums of i.i.d. random variables to an a-stable Levy motio...
We discuss three forms of convergence in distribution which are stronger than the normal weak conver...
We discuss three forms of convergence in distribution which are stronger than the normal weak conver...
The purpose of this course was to present results on weak convergence and invariance principle with ...
AbstractWeak convergence of probability measures on function spaces has been active area of research...
We know that almost sure convergence of random variables implies their convergence in distribution. ...
Summary. Subspaces Da, a> 0, of D[0, 1] are defined and given cor~p!ete metrics d~ which are stro...
We know that almost sure convergence of random variables implies their convergence in distribution. ...
Subspaces D α , α > 0, of D [0, 1] are defined and given complete metrics d α which are stronger tha...
AbstractEarlier results on weak convergence to diffusion processes [8] are generalized to cases wher...
We consider the weak convergence of numerical methods for stochastic differential equations (SDEs). ...
The hypo-convergence of upper semicontinuous functions provides a natural framework for the study of...
Let {X(n), n greater-than-or-equal-to 1} be a sequence of independent random variables and M(n) = ma...
The weak convergence of the empirical process of strong mixing or associated random variables is stu...
none3noGiven a sequence (mn) of random probability measures on a metric space S, consider the condit...
Skorohod has shown that the convergence of sums of i.i.d. random variables to an a-stable Levy motio...
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