两两NQD列的Marcinkiewicz-Zygmund不等式及其应用（英文）

本文研究了两两NQD随机变量的Marcinkiewicz-Zygmund不等式及其应用的问题.利用截尾的方法,获得了两两NQD随机变量的p阶(1≤p2)Marcinkiewicz-Zygmund不等式结果.作为应用,获得了两两NQD随机变量的两个Lr收敛性结果的简单证明,改进了陈平炎[10]和Sung[20]的相应工作.     

φ-混合序列加权和的完全收敛性

该文把Chen和Sung (文献[1])的一个关于同分布NA随机变量序列加权和最大值完全收敛性结果推广到了φ-混合随机变量序列情形.由于已有文献所用的工具本质上是部分和最大值指数型概率不等式,而对于φ-混合随机变量序列而言,没有那么好的指数型不等式,因此原有的证明方法已失效.该文将应用φ-混合随机变量序列部分和最大值的2-阶Marcinkiewicz-Zygmund矩不等式,结合再截尾方法,获得了理想的结果.该文的证明方法不同于已有结果的证明方法.     

两两NQD列的Marcinkiewicz-Zygmund不等式及其应用

本文研究了两两NQD随机变量的Marcinkiewicz-Zygmund不等式及其应用的问题.利用截尾的方法,获得了两两NQD随机变量的p阶(1 ≤ p < 2) Marcinkiewicz-Zygmund不等式结果.作为应用,获得了两两NQD随机变量的两个L^r收敛性结果的简单证明,改进了陈平炎^[10]和Sung^[20]的相应工作.     

NA随机变量加权和的收敛性

利用Rosenthal型最大值不等式,得到了NA随机变量加权和的Marcinkiewicz-Zygmund强大数定律和完全收敛性,所获结果推广和改进了一些文献中相应的结果.     

鞅差序列的概率不等式和Rosenthal不等式（英文）

本文研究了鞅差序列的一些不等式.利用条件期望性质和基本不等式,获得了鞅差序列的Bernstein, Kolmogorov和Hoeffding不等式,推广了有界随机向量相应的结果.另外,得到了鞅差序列的最大部分和的经典Kolmogorov和Rosenthal不等式,补充了次线性期望下独立和负相依随机变量的相应结果.     

Chebyshev不等式的注记

Chebyshev不等式是一个重要的概率不等式.从几何图形的角度,巧妙地证明了Chebyshev不等式,论证了不等式中等号成立的条件,并借此构造了一类使Chebyshev不等式中等号成立的随机变量.     

不等式证明的概率方法

运用概率论中的Jensen不等式,并且适当地构造随机变量的方法证明不等式,使得不等式的证明变得简单、清晰,同时使得不等式具有某种概率统计意义.     

行为ND随机变量阵列加权和的完全收敛性

在非同分布的情况下,给出了行为ND随机变量阵列加权和的完全收敛性的充分条件,所得结果部分地推广了独立随机变量和NA随机变量的相应结果.作为其应用,获得了ND随机变量序列加权和的Marcinkiewicz-Zygmund型强大数定律.     

END序列加权和的完全收敛性

该文把Sung~([1])的一个关于同分布NA随机变量序列加权和最大值完全收敛性结果部分推广到了END随机变量序列情形.由于已有文献所用的工具是部分和最大值指数型不等式或部分和最大值Rosenthal型矩不等式,而对于END而言相应的不等式是否成立至今未知,因此原有的证明方法已失效.该文将应用END随机变量序列部分和的Rosenthal型矩不等式,结合三段截尾法,获得了理想的结果.该文的证明方法不同于已有的结果.     

多元非正规样本定理

本文研究了多元指数型整函数在非等距节点的Marcinkiewicz-Zygmund型不等式.据此,得到了多元非正规样本定理.     

Rosenthal’s inequalities for independent and negatively dependent random variables under sub-linear expectations with applications

Classical Kolmogorov’s and Rosenthal’s inequalities for the maximum partial sums of random variables are basic tools for studying the strong laws of large numbers. In this paper, motived by the notion of independent and identically distributed random variables under the sub-linear expectation initiated by Peng (2008), we introduce the concept of negative dependence of random variables and establish Kolmogorov’s and Rosenthal’s inequalities for the maximum partial sums of negatively dependent random variables under the sub-linear expectations. As an application, we show that Kolmogorov’s strong law of larger numbers holds for independent and identically distributed random variables under a continuous sub-linear expectation if and only if the corresponding Choquet integral is finite.     

独立的无界随机变量和的概率不等式

研究了在概率空间(Ω,T,P)上,独立的无界随机变量和尾部概率不等式,提出了一种用切割原始概率空间(Ω,T,P)的新型方法去处理独立的无界随机变量和。给出了独立的无界随机变量和的指数型概率不等式。作为结果的应用,一些有趣的例子被给出。这些例子表明:文中提出的方法和结果对研究独立的无界随机变量和的大样本性质是十分有用的。     

NA随机变量列的有界重对数律

通过建立NA随机变量最大部分和的一些概率指数不等式，给出了具有不同分布的NA随机变量列有界重对数律的一些结果，因此推广了由R．Wittmann建立的独立随机变量的相关结果。     

Exponential inequalities under the sub-linear expectations with applications to laws of the iterated logarithm

Kolmogorov’s exponential inequalities are basic tools for studying the strong limit theorems such as the classical laws of the iterated logarithm for both independent and dependent random variables. This paper establishes the Kolmogorov type exponential inequalities of the partial sums of independent random variables as well as negatively dependent random variables under the sub-linear expectations. As applications of the exponential inequalities, the laws of the iterated logarithm in the sense of non-additive capacities are proved for independent or negatively dependent identically distributed random variables with finite second order moments. For deriving a lower bound of an exponential inequality, a central limit theorem is also proved under the sub-linear expectation for random variables with only finite variances.     

Hoeffding’s Inequality for Sums of Dependent Random Variables

Let \(X_1,\ldots ,X_n\) be, possibly dependent, [0, 1]-valued random variables. What is a sharp upper bound on the probability that their sum is significantly larger than their mean? In the case of independent random variables, a fundamental tool for bounding such probabilities is devised by Wassily Hoeffding. In this paper, we provide a generalisation of Hoeffding’s theorem. We obtain an estimate on the aforementioned probability that is described in terms of the expectation, with respect to convex functions, of a random variable that concentrates mass on the set \(\{0,1,\ldots ,n\}\). Our main result yields concentration inequalities for several sums of dependent random variables such as sums of martingale difference sequences, sums of k-wise independent random variables, as well as for sums of arbitrary [0, 1]-valued random variables.     

The Convergence of the Sums of Independent Random Variables Under the Sub-linear Expectations

Let {Xn;n≥1} be a sequence of independent random variables on a probability space(Ω,F,P) and Sn=∑k=1n Xk.It is well-known that the almost sure convergence,the convergence in probability and the convergence in distribution of Sn are equivalent.In this paper,we prove similar results for the independent random variables under the sub-linear expectations,and give a group of sufficient and necessary conditions for these convergence.For proving the results,the Levy and Kolmogorov maximal inequalities for independent random variables under the sub-linear expectation are established.As an application of the maximal inequalities,the sufficient and necessary conditions for the central limit theorem of independent and identically distributed random variables are also obtained.     

The laws of large numbers for Pareto-type random variables under sub-linear expectation

In this paper, some laws of large numbers are established for random variables that satisfy the Pareto distribution, so that the relevant conclusions in the traditional probability space are extended to the sub-linear expectation space. Based on the Pareto distribution, we obtain the weak law of large numbers and strong law of large numbers of the weighted sum of some independent random variable sequences.     

Rosenthal’s Inequalities for Asymptotically Almost Negatively Associated Random Variables Under Upper Expectations

In this paper, the authors generalize the concept of asymptotically almost \linebreak negatively associated random variables from the classic probability space to the upper expectation space. Within the framework, the authors prove some different types of Rosenthal''s inequalities for sub-additive expectations. Finally, the authors prove a strong law of large numbers as the application of Rosenthal''s inequalities.     

Banach空间的型与独立Banach空间值随机变量序列的不等式

本文建立了独立Ｂ值随机变量序列部分和的极大值函数Ｓ＊与ｐ方根函数Ｓ（ｐ，Ｘ）的分布函数不等式与矩不等式，讨论了这些不等式的成立与Ｂａｎａｃｈ空间的ｐ型与ｑ余型的关系，同时给出了与Ｈｉｌｂｅｒｔ空间同构的Ｂａｎａｃｈ空间的特征。     

m-NA随机阵列的完全收敛性的一个注记

本文研究了行m-NA随机阵列的完全收敛性.利用文[8]中结果获得了m-NA列最大部分和的一个概率不等式,并根据该不等式和截尾的方法,探讨了行m-NA随机阵列的完全收敛性,获得了与行NA随机阵列情形类似的结果,简化了文[5]中定理1的证明.