Mixing properties and central limit theorem for associated point processes

The central limit theorem (1.12) for the simple exclusion process was established by Ferrari and Fonte [5] in two cases, either when the η-process is in equilibrium, or when g(x) is

Finally, the semidiscrete framework provides a control on the second derivative of the dual formulation, which yields the first central limit theorem for the optimal

However, our ap- proach to prove uniquess and stability of optimal transportation potentials relies on tools from the theory of graphical convergence of multivalued maps (a

Debbasch, Malik and Rivet introduced in [DMR] a relativistic diffusion in Minkowski space, they called Relativistic Ornstein-Uhlenbeck Process (ROUP), to describe the motion of a

Key words and phrases: Central limit theorem, spatial processes, m- dependent random fields, weak mixing..

In the context of this work, we consider functionals which act on these clusters of relevant values and we develop useful lemmas in order to simplify the essential step to establish

In study of the central limit theorem for dependent random variables, the case of martingale difference sequences has played an important role, cf.. Hall and

exponential Young funtions for stationary real random elds whih are bounded.. or satisfy some nite exponential