Some Results on Lipschitzian Stochastic Differential Equations by Dirichlet Forms Methods.

Key words: Non-parametric regression, Weighted least squares esti- mates, Improved non-asymptotic estimation, Robust quadratic risk, L´ evy process, Ornstein – Uhlenbeck

The corresponding error analysis consists of a statistical error induced by the central limit theorem, and a discretization error which induces a biased Monte Carlo

Depuis 1975, la Cour de cassation est revenue sur cette conception restrictive : « Attendu que, sans doute, l’article 1385 du Code civil ne subor- donne pas le transfert de

An original feature of the proof of Theorem 2.1 below is to consider the solutions of two Poisson equations (one related to the average behavior, one related to the

Meanwhile it has become widely recognized by Governments that bringing developed country emissions back to their AD 1990 levels by the year 2000 will not be enough to achieve

But in the applications, for studying the sensitivity of stochastic models, we are mostly concerned with computations in situations where an error structure is defined on the

In this paper, we consider a natural extension of the Dirichlet-to-Neumann operator T to an elliptic operator T [p] acting on differential forms of arbitrary degree p on the boundary

Classical approaches for getting high weak order numerical schemes for stochastic differential equations are based on weak Taylor ap- proximation or Runge-Kutta type methods [8,