The classical Gaussian concentration inequality for Lipschitz functions is adapted to a setting where the classical assumptions (i.e. Lipschitz and Gaussian) are not met. The theory is more direct than much of the existing theory designed to handle related generalizations. An application is presented to linear combinations of heavy tailed random variables.https://www.tandfonline.com/loi/tqma20hj2023Mathematics and Applied Mathematic
In this paper we improve the rate function in the McDiarmid concentration inequality for separately ...
24 pagesWe show how to use the Malliavin calculus to obtain density estimates of the law of general ...
Summary. Sharp lower bounds are found for the concentration of a probability distribution as a funct...
The classical Gaussian concentration inequality for Lipschitz functions is adapted to a setting wher...
Concentration inequalities are fundamental tools in probabilistic combinatorics and theoretical comp...
International audienceNon-Gaussian concentration estimates are obtained for invariant probability me...
International audienceNon-Gaussian concentration estimates are obtained for invariant probability me...
We prove analogues of the popular bounded difference inequality (also called McDiarmid’s inequality)...
International audienceNon-Gaussian concentration estimates are obtained for invariant probability me...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
International audienceWe consider the full shift $T:\Omega\to\Omega$ where $\Omega=A^{\mathbb{N}}$, ...
International audienceWe consider the full shift $T:\Omega\to\Omega$ where $\Omega=A^{\mathbb{N}}$, ...
We obtain concentration and large deviation for the sums of independent and identically distributed ...
Let \( X \) be a Gaussian zero mean vector with \( Var(X) = B \). Then \( \| X \|^{2} \) well concen...
In this paper we improve the rate function in the McDiarmid concentration inequality for separately ...
24 pagesWe show how to use the Malliavin calculus to obtain density estimates of the law of general ...
Summary. Sharp lower bounds are found for the concentration of a probability distribution as a funct...
The classical Gaussian concentration inequality for Lipschitz functions is adapted to a setting wher...
Concentration inequalities are fundamental tools in probabilistic combinatorics and theoretical comp...
International audienceNon-Gaussian concentration estimates are obtained for invariant probability me...
International audienceNon-Gaussian concentration estimates are obtained for invariant probability me...
We prove analogues of the popular bounded difference inequality (also called McDiarmid’s inequality)...
International audienceNon-Gaussian concentration estimates are obtained for invariant probability me...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
International audienceWe consider the full shift $T:\Omega\to\Omega$ where $\Omega=A^{\mathbb{N}}$, ...
International audienceWe consider the full shift $T:\Omega\to\Omega$ where $\Omega=A^{\mathbb{N}}$, ...
We obtain concentration and large deviation for the sums of independent and identically distributed ...
Let \( X \) be a Gaussian zero mean vector with \( Var(X) = B \). Then \( \| X \|^{2} \) well concen...
In this paper we improve the rate function in the McDiarmid concentration inequality for separately ...
24 pagesWe show how to use the Malliavin calculus to obtain density estimates of the law of general ...
Summary. Sharp lower bounds are found for the concentration of a probability distribution as a funct...