A strong law of large numbers for harmonizable isotropic random fields.
A strong law of large numbers for identically distributed vector lattice-valued random variables
A strong limit theorem for functions of continuous random variables and an extension of the Shannon-McMillan theorem.
A strong limit theorem for weighted sums of sequences of negatively dependent random variables.
A Subsequence Principle in Probability Theory. II. The Law of the Iterated Logarithm.
A super-stable motion with infinite mean branching
A survey of limit laws for bootstrapped sums.
A survey on the general central limit problem in Banach spaces
A Tauberian theorem for increasing functions of dominated variation.
A two armed bandit type problem revisited
In Benaïm and Ben Arous (2003) is solved a multi-armed bandit problem arising in the theory of learning in games. We propose a short and elementary proof of this result based on a variant of the Kronecker lemma.
A two armed bandit type problem revisited
In Benaïm and Ben Arous (2003) is solved a multi-armed bandit problem arising in the theory of learning in games. We propose a short and elementary proof of this result based on a variant of the Kronecker lemma.
A uniform central limit theorem for dependent variables
Niemiro and Zieliński (2007) have recently obtained uniform asymptotic normality for the Bernoulli scheme. This paper concerns a similar problem. We show the uniform central limit theorem for a sequence of stationary random variables.
A uniform estimate for the rate of convergence in the multidimensional central limit theorem for homogeneous Markov chains.
A Uniform Law of the Iterated Logarithm for Brownian Motion on Compact Riemannian Manifolds.
A universality property for last-passage percolation paths close to the axis.
A Useful Central Limit Theorem for m-Dependent Variables
A version of the Harris-Spitzer "random constant velocity" model for infinite systems of particles
A version of the law of large numbers
By the method of Rio [10], for a locally square integrable periodic function f, we prove for almost every x and t > 0.
A very elementary proof of a probabilistic limit relation.
A weak law of large numbers for the sample covariance matrix.