AbstractThis paper derives a sharp bound for the probability that a sum of independent symmetric random vectors lies in a symmetric convex set. In one dimension this bound is an improvement of an inequality first proved by Kolmogorov. The subject of multidimensional concentration functions is also treated
This paper establishes new concentration inequalities for random matrices constructed from independe...
This paper deduces exponential matrix concentration from a Poincaré inequality via a short, conceptu...
We consider a random variable X that takes values in a (possibly infinite-dimensional) topological v...
AbstractWe consider estimating the mean θ of an n dimensional normal vector X with the restriction t...
Cette thèse porte sur l'étude de la concentration autour de la moyenne de fonctions de variables alé...
In this paper we consider a sum of modified Bessel functions of the first kind of which particular c...
The paper deals with studying a connection of the Littlewood--Offord problem with estimating the con...
Slepian and Sudakov-Fernique type inequalities, which compare expectations of maxima of Gaussian ran...
Our goal is to write an extended version of the notes of a course given by Olivier Guédon at the Po...
This thesis deals with concentration properties around the mean of functions of independent random v...
AbstractWe consider estimating the mean θ of an n dimensional normal vector X with the restriction t...
This thesis deals with concentration properties around the mean of functions of independent random v...
This thesis deals with concentration properties around the mean of functions of independent random v...
Summary. Sharp lower bounds are found for the concentration of a probability distribution as a funct...
We consider estimating the mean [theta] of an n dimensional normal vector X with the restriction tha...
This paper establishes new concentration inequalities for random matrices constructed from independe...
This paper deduces exponential matrix concentration from a Poincaré inequality via a short, conceptu...
We consider a random variable X that takes values in a (possibly infinite-dimensional) topological v...
AbstractWe consider estimating the mean θ of an n dimensional normal vector X with the restriction t...
Cette thèse porte sur l'étude de la concentration autour de la moyenne de fonctions de variables alé...
In this paper we consider a sum of modified Bessel functions of the first kind of which particular c...
The paper deals with studying a connection of the Littlewood--Offord problem with estimating the con...
Slepian and Sudakov-Fernique type inequalities, which compare expectations of maxima of Gaussian ran...
Our goal is to write an extended version of the notes of a course given by Olivier Guédon at the Po...
This thesis deals with concentration properties around the mean of functions of independent random v...
AbstractWe consider estimating the mean θ of an n dimensional normal vector X with the restriction t...
This thesis deals with concentration properties around the mean of functions of independent random v...
This thesis deals with concentration properties around the mean of functions of independent random v...
Summary. Sharp lower bounds are found for the concentration of a probability distribution as a funct...
We consider estimating the mean [theta] of an n dimensional normal vector X with the restriction tha...
This paper establishes new concentration inequalities for random matrices constructed from independe...
This paper deduces exponential matrix concentration from a Poincaré inequality via a short, conceptu...
We consider a random variable X that takes values in a (possibly infinite-dimensional) topological v...
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