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Abstract A common technique in the theory of stochastic process is to replace a discrete time coordinate by a continuous randomized time, defined by an independent Poisson or
The case of Markov chains was first considered by Marton [20; 21] : for uniformly contracting Markov chains, in particular for ergodic Markov chains with finite state space,
Abstract: In this paper we give a solution for the one-dimensional reflected backward stochastic differential equation when the noise is driven by a Brownian motion and an
We consider the optimal mass transportation problem in R d with measurably parameterized marginals under conditions ensuring the existence of a unique optimal transport map.. We prove
We will prove that if a limit measure is not absolutely continuous with respect to the Lebesgue measure then the corresponding random walk on the self similar graph does not have
A recent theorem in [3] provided a link between a certain function of transition probabilities of a strong Markov process and the boundedness of the p -variation of its
We prove here (by showing convergence of Chen’s series) that linear stochastic differential equa- tions driven by analytic fractional Brownian motion [6, 7] with arbitrary Hurst index
Keywords: Total variation distance; Non-central limit theorem; Fractional Brownian motion; Hermite power variation; Multiple stochastic integrals; Hermite random
