次线性期望下的一般中心极限定理

受Peng-中心极限定理的启发,本文主要应用G-正态分布的概念,放宽Peng-中心极限定理的条件,在次线性期望下得到形式更为一般的中心极限定理.首先,将均值条件E[X_n]=ε[X_n]=0放宽为|E[X_n]|+|ε[X_n]|=O(1/n);其次,应用随机变量截断的方法,放宽随机变量的2阶矩与2+δ阶矩条件;最后,将该定理的Peng-独立性条件进行放宽,得到卷积独立随机变量的中心极限定理.     

次线性期望下的若干矩不等式

本文在Peng建立的次线性期望空间下证明了Bernstein不等式,Kolmogorov不等式以及Rademacher不等式.进一步,本文分别应用Bernstein不等式、Kolmogorov不等式以及Rademacher不等式对次线性期望空间下随机变量列的拟必然收敛性质进行了深入研究,并得到了相应的强收敛定理.     

非线性概率下行内负相协随机变量阵列的对数律

本文在上概率空间中给出随机变量负相协的定义,该定义弱于现有非线性概率下的某些独立性概念.在此框架下,本文通过对随机变量阵列收敛性质的研究,得到上概率下行内负相协随机变量阵列的对数律,并同时给出依容度收敛的弱对数律.     

Laws of Large Numbers for Arrays of Rowwise Negatively Associated Random Variables under Nonlinear Probabilities北大核心CSCD

In this paper, a strong law of large numbers for arrays of rowwise negatively associated random variables is obtained under nonlinear probabilities, from which Kolmogorov type and Marcinkiewicz–Zygmund type strong laws of large numbers are derived. And the notion of negative association is weaker than some existing notions of dependence in nonlinear probabilities. Furthermore, an extension of strong law of large numbers for arrays of rowwise independent random variables under nonlinear probabilities is obtained. As a special case, a Kolmogorov type strong law indicates that not only the cluster points of empirical averages lie in the interval between the lower expectation and upper expectation quasi-surely, but such an interval is also the smallest one that covers the empirical averages quasi-surely. Furthermore, the strong law also states that the upper and lower limits of the empirical averages will converge to the upper and lower expectations with upper probabilities one, respectively. © 2022 Chinese Academy of Sciences. All rights reserved.