ترغب بنشر مسار تعليمي؟ اضغط هنا

Product space for two processes with independent increments under nonlinear expectations

123   0   0.0 ( 0 )
 نشر من قبل Mingshang Hu
 تاريخ النشر 2016
  مجال البحث
والبحث باللغة English




اسأل ChatGPT حول البحث

In this paper, we consider the product space for two processes with independent increments under nonlinear expectations. By introducing a discretization method, we construct a nonlinear expectation under which the given two processes can be seen as a new process with independent increments.

قيم البحث

اقرأ أيضاً

167 - F. Klebaner , R. Liptser 2005
We consider a continuous time version of Cramers theorem with nonnegative summands $ S_t=frac{1}{t}sum_{i:tau_ile t}xi_i, t toinfty, $ where $(tau_i,xi_i)_{ige 1}$ is a sequence of random variables such that $tS_t$ is a random process with independent increments.
In this paper, we study unitary Gaussian processes with independent increments with which the unitary equivalence to a Hudson-Parthasarathy evolution systems is proved. This gives a generalization of results in [16] and [17] in the absence of the stationarity condition.
This is a continuation of the earlier work cite{SSS} to characterize stationary unitary increment Gaussian processes. The earlier assumption of uniform continuity is replaced by weak continuity and with a technical assumption on the domain of the gen erator, unitary equivalence of the processes to the solution of Hudson-Parthasarathy equation is proved.
The aim of this article is to characterize unitary increment process by a quantum stochastic integral representation on symmetric Fock space. Under certain assumptions we have proved its unitary equivalence to a Hudson-Parthasarathy flow.
Under the sublinear expectation $mathbb{E}[cdot]:=sup_{thetain Theta} E_theta[cdot]$ for a given set of linear expectations ${E_theta: thetain Theta}$, we establish a new law of large numbers and a new central limit theorem with rate of convergence. We present some interesting special cases and discuss a related statistical inference problem. We also give an approximation and a representation of the $G$-normal distribution, which was used as the limit in Peng (2007)s central limit theorem, in a probability space.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا