In this thesis, we make a contribution to the study of the properties of the generalized inverse Gaussian and Kummer distributions in the context of Stein's method. This is to contribute to establishing the mathematical tools necessary for the application of Stein's method to the case where the target law is one of the two aforementioned laws, on the one hand, and to applying Stein's method to give a bound of the rate of convergence in some limit theorems involving these two laws, on the other hand. We retrieve the Stein operator of each of these two laws, solve the corresponding differential equation and bound the solution obtained as well as its successive derivatives (for the derivatives, the bound is not always explicit but is establish...
The Stein's method is a collection of probabilistic techniques for answering the ques- tion as to ho...
AbstractThe problem of global estimation of the mean function θ(·) of a quite arbitrary Gaussian pro...
In view of their importance and usefulness in reliability theory and probability distributions, seve...
We observe that the density of the Kummer distribution satisfies a certain differential equation, le...
A sequence of random variables following the generalized inverse Gaussian or the Kummer distribution...
For regular test functions h, we prove that all the derivatives of the solution of the Stein equatio...
International audienceNous apportons une contribution à l'étude des propriétés des lois gaussiennes ...
The generalized hyperbolic (GH) distributions form a five parameter family of probability distributi...
Stein's method is a powerful technique that can be used to obtain bounds for approximation errors in...
International audienceWe propose a brief survey of characterizations of the Generalized Inverse Gaus...
In extending Stein's method to new target distributions, the first step is to find a Stein operator ...
Using coupling techniques based on Stein's method for probability approximation, we revisit classica...
Stein operators are (differential/difference) operators which arise within the so-called Stein's met...
AbstractStein's method provides a way of finding approximations to the distribution, ρ say, of a ran...
Stein's method provides a way of finding approximations to the distribution, ¿ say, of a random vari...
The Stein's method is a collection of probabilistic techniques for answering the ques- tion as to ho...
AbstractThe problem of global estimation of the mean function θ(·) of a quite arbitrary Gaussian pro...
In view of their importance and usefulness in reliability theory and probability distributions, seve...
We observe that the density of the Kummer distribution satisfies a certain differential equation, le...
A sequence of random variables following the generalized inverse Gaussian or the Kummer distribution...
For regular test functions h, we prove that all the derivatives of the solution of the Stein equatio...
International audienceNous apportons une contribution à l'étude des propriétés des lois gaussiennes ...
The generalized hyperbolic (GH) distributions form a five parameter family of probability distributi...
Stein's method is a powerful technique that can be used to obtain bounds for approximation errors in...
International audienceWe propose a brief survey of characterizations of the Generalized Inverse Gaus...
In extending Stein's method to new target distributions, the first step is to find a Stein operator ...
Using coupling techniques based on Stein's method for probability approximation, we revisit classica...
Stein operators are (differential/difference) operators which arise within the so-called Stein's met...
AbstractStein's method provides a way of finding approximations to the distribution, ρ say, of a ran...
Stein's method provides a way of finding approximations to the distribution, ¿ say, of a random vari...
The Stein's method is a collection of probabilistic techniques for answering the ques- tion as to ho...
AbstractThe problem of global estimation of the mean function θ(·) of a quite arbitrary Gaussian pro...
In view of their importance and usefulness in reliability theory and probability distributions, seve...