AbstractBivariate stable distributions are defined as those having a domain of attraction, where vectors are used for normalization. These distributions are identified and their domains of attraction are given in a number of equivalent forms. In one case, marginal convergence implies joint convergence. A bivariate optional stopping property is given. Applications to bivariate random walk are suggested
This paper deals with multivariate stable distributions. [6], 444-462]. We present counter-examples ...
Multivariate symmetric stable characteristic functions and their properties, as well as conditions f...
A new class of bivariate distributions is introduced and studied, which encompasses Archimedean copu...
Many statistics are based on functions of sample moments. Important examples are the sample variance...
A sequence of independent, identically distributed random vectors X1, X2, X3,... is said to belong t...
Multivariate local limit theorems are established for sums of random variables which are in the doma...
AbstractThis paper is devoted to the theory and application of multidimensional stable distributions...
AbstractWith (X1, X2) in a stable domain of attraction and (Y1, Y2) independent of (X1, X2), conditi...
A sequence of independent, identically distributed random vectors Xl> X 2, X 3, ••• is said to be...
õ0.ABSTRACT. Let X be a random variable with density function which is continuous and nonzero at 0. ...
AbstractA new characterization of the bivariate Marshall-Olkin distribution is presented: it is show...
AbstractA sequence of independent, identically distributed random vectors X1, X2, X3,… is said to be...
A sequence of the independent, identically distributed r and om vectors X(,1),X(,2),X(,3),..., is sa...
The tail behaviour of many bivariate distributions with unit Fréchet margins can be characterised by...
"By definition any stable distribution is semistable. For the converse relation we will show that ce...
This paper deals with multivariate stable distributions. [6], 444-462]. We present counter-examples ...
Multivariate symmetric stable characteristic functions and their properties, as well as conditions f...
A new class of bivariate distributions is introduced and studied, which encompasses Archimedean copu...
Many statistics are based on functions of sample moments. Important examples are the sample variance...
A sequence of independent, identically distributed random vectors X1, X2, X3,... is said to belong t...
Multivariate local limit theorems are established for sums of random variables which are in the doma...
AbstractThis paper is devoted to the theory and application of multidimensional stable distributions...
AbstractWith (X1, X2) in a stable domain of attraction and (Y1, Y2) independent of (X1, X2), conditi...
A sequence of independent, identically distributed random vectors Xl> X 2, X 3, ••• is said to be...
õ0.ABSTRACT. Let X be a random variable with density function which is continuous and nonzero at 0. ...
AbstractA new characterization of the bivariate Marshall-Olkin distribution is presented: it is show...
AbstractA sequence of independent, identically distributed random vectors X1, X2, X3,… is said to be...
A sequence of the independent, identically distributed r and om vectors X(,1),X(,2),X(,3),..., is sa...
The tail behaviour of many bivariate distributions with unit Fréchet margins can be characterised by...
"By definition any stable distribution is semistable. For the converse relation we will show that ce...
This paper deals with multivariate stable distributions. [6], 444-462]. We present counter-examples ...
Multivariate symmetric stable characteristic functions and their properties, as well as conditions f...
A new class of bivariate distributions is introduced and studied, which encompasses Archimedean copu...