独立同分布随机变量加权和的概率估计

设{ξ_i}_i=1ⁿ为独立同分布的随机变量,且P(ξ_i=1)=P(ξ_i=-1)=1/2.设a=(a₁,…,a_n)为与{ξⁱ}_i=1ⁿ独立的服从超球面S^n-1={(a₁,…,a_n)∈Rⁿ|∑_i-1ⁿ a_i²=1}上均匀分布的随机变量,该文用极坐标变换得到了P(|∑_i=1ⁿ a_iξ_i|≤1)的表达式.当n<7时,该文通过直接计算得到此概率值大于等于1/2;当n≥8时,该文通过R软件也得到了此概率值大于等于1/2.特别地,n=3,4时,借助于贝塔函数,该文直接证明了该概率值大于等于1/2.     

关于确定两个非独立随机变量和分布方法的探讨

本文通过示例研究和探讨了确定两个非独立随机变量和分布的方法.     

次线性期望下独立同分布随机变量序列加权和的完全收敛的等价条件

本文研究了次线性期望空间下独立同分布随机变量序列加权和的完全收敛性.利用矩不等式和截尾方法,我们证明了次线性期望空间下独立同分布随机变量序列加权和完全收敛的等价条件.这些在次线性期望下独立同分布随机变量序列的结果补充了概率空间中的相应结果.     

关于非负随机变量的几个矩不等式

设X是非负随机变量且0相似文献   

独立情形下自正则和的精确渐近性

研究均值为零非退化的独立同分布的随机变量序列正则和收敛性,在适当条件下,获得了自正则和精确渐近性的一般结果.     

关于独立同分布正态随机变量部分和尾概率的注(英文)

设{X,Xn,n≥1}是独立同分布正态随机变量序列,EX=0且EX2=σ2>0,Sn=sum (Xk) form k=1 to n,λ(ε) =sum form (P(|Sn|≥ nε)) form n=1 to ∞.在本文中,我们证明了存在正常数C1和C2,使得对足够小的ε>0,成立下列不等式C1ε3 ≤ε2λ(ε)-σ2+ε2 /2 ≤ C2ε3.     

关于非独立随机变量序列部分和的一个不等式及其应用

本文讨论的是一般随机变量部分和的处理方法,得到了非独立随机变量部分和的分布的一个不等式并给出了它的应用,证明了非负有界随机变量序列的部分和的收敛与它的相应的条件期望序列的部分和的收敛等价。     

次线性期望下独立同分布序列的一般强收敛性

在Choquet积分存在条件下,研究并建立次线性期望空间中的独立同分布随机变量序列的一般强收敛性定理,从而将传统概率空间的一般强收敛定理推广到次线性期望空间中.我们的结果推广了MENG(2019)的相应结果,得到两个一般的强大数定律(SLLN),其中加权和的系数是一般函数,作为推论,我们得到独立同分布随机变量序列的Marcinkiewicz型SLLN、对数SLLN和Marcinkiewicz SLLN.     

在条件lim(n→∞)(infP(X_n= 0)>0)下的一类独立随机变量和的收敛定理

设｛Ｘ_ｎ,ｎ≥ １｝是一独立随机变量序列．受概率数论中Ｅｒｄｏｓ猜想的启发,我们研究了在条件lim(n→∞)(infP(X_n= 0)>0)下的独立项级数ｓｕｍ　ｆｒｏｍ　ｎ＝１ Ｘ_ｎ的 ａ．ｓ．收敛性,并且获得了该级数ａ．ｓ．收敛的两个充分必要条件和一个充分条件．这些定理分别改进了文献［３］、［５］中关于Ｅｒｄｏｓ猜想的研究结果．     

Stability of Maxima of Random Variables with Multidimensional Indices

The paper discusses the stability of suitably-defined maxima of a set of i.i.d. random variables with multidimensional indices.It is shown that theorems of Gnedenko (1943) and Tomkins (1986) concerning relative stability and complete relative stability of maxima remain valid in the new setting.Moreover, a criterion for almost sure relative stability for maxima with multidimensional indices is presented, extending a result of Barndorff-Nielsen (1963).AMS 2000 Subject Classification. Primary—60F15, 60G60, 62G30, Secondary—10A25, 60G99     

随机变量数学期望的一个注记

介绍一种计算随机变量数学期望的方法,利用这种方法容易得到数学期望的相关性质,很多概率与矩的不等式证明也因之变得更为简洁.     

对称随机变量部分和的概率不等式

在对称随机变量分布函数关于原点的值大于或等于二分之一的基础上,阐明对称随机变量的部分和仍是对称随机变量,进一步,给出关于对称随机变量序列部分和的概率不等式.     

多指标随机变量Marcinkiewicz大数定律完全收敛性和收敛速度

本文考虑指标在，ｄ≥１中的独立同分布随机变量序列，得到了有关大数定律的完全收敛性和收敛速度等一些结果．     

Precise Rates in the Law of Iterated Logarithm for the Moment of I.I.D. Random Variables

Let{X,Xn;n≥1} be a sequence of i,i.d, random variables, E X = 0, E X^2 = σ^2 〈 ∞.Set Sn=X1＋X2＋…＋Xn,Mn=max k≤n│Sk│,n≥1.Let an=O（1/loglogn）.In this paper,we prove that,for b〉-1,lim ε→0 →^2（b＋1）∑n=1^∞ （loglogn）^b/nlogn n^1/2 E{Mn-σ（ε＋an）√2nloglogn}＋σ2^-b/（b＋1）（2b＋3）E│N│^2b＋3∑k=0^∞ （-1）k/（2k＋1）^2b＋3 holds if and only if EX=0 and EX^2=σ^2〈∞.     

On the Rate of Decay of the Concentration Function of the Sum of Independent Random Variables

Let X₁, ... , X_n be i.i.d. integral valued random variables and S_n their sum. In the case when X₁ has a moderately large tail of distribution, Deshouillers, Freiman and Yudin gave a uniform upper bound for max _{k ∊ ℤ} Pr{S_n = k} (which can be expressed in term of the Lévy Doeblin concentration of S_n), under the extra condition that X₁ is not essentially supported by an arithmetic progression. The first aim of the paper is to show that this extra condition cannot be simply ruled out. Secondly, it is shown that if X₁ has a very large tail (larger than a Cauchy-type distribution), then the extra arithmetic condition is not sufficient to guarantee a uniform upper bound for the decay of the concentration of the sum S_n. Proofs are constructive and enhance the connection between additive number theory and probability theory.À Jean-Louis Nicolas, avec amitié et respect2000 Mathematics Subject Classification: Primary—60Fxx, 60Exx, 11Pxx, 11B25     

Asymptotic Distribution of Extremes of Randomly Indexed Random Variables

Let {X _n} be a sequence of i.i.d. random variables and let {_k} be a sequence of random indexes. We study the problem of the existence of non-degenerated asymptotic distribution for min{X ₁,..., X _n}.     

Precise rates in the law of logarithm for the moment convergence of i.i.d. random variables

Let be a sequence of i.i.d. random variables with EX=0 and EX²=σ²<∞. Set , Mn=maxk?n|Sk|, n?1. Let r>1, then we obtain

     

A Comparison Theorem on Moment Inequalities Between Negatively Associated and Independent Random Variables

Let {X _i, 1in} be a negatively associated sequence, and let {X* _i , 1in} be a sequence of independent random variables such that X* _i and X _i have the same distribution for each i=1, 2,..., n. It is shown in this paper that Ef( ⁿ _i=1 X _i)Ef( ⁿ _i=1 X* _i ) for any convex function f on R ¹ and that Ef(max_1kn ⁿ _i=k X _i)Ef(max_1kn ^k _i=1 X* _i ) for any increasing convex function. Hence, most of the well-known inequalities, such as the Rosenthal maximal inequality and the Kolmogorov exponential inequality, remain true for negatively associated random variables. In particular, the comparison theorem on moment inequalities between negatively associated and independent random variables extends the Hoeffding inequality on the probability bounds for the sum of a random sample without replacement from a finite population.     

A Note on the Almost Sure Convergence for Dependent Random Variables in a Hilbert Space

We obtain the almost sure convergence for sequences of H-valued random variables which are either associated or negatively associated. Our results extend the results of Birkel (Stat. Probab. Lett. 7:17–20, 1989) and Matula (Stat. Probab. Lett. 15:209–213, 1992) to a Hilbert space.