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．     

一类随机变量序列部分和的强大数定律

设{Xn,n≥1}是随机变量序列.文[4]在二阶矩限制下,获得了任意随机变量序列的Hajek-Renyi型不等式,并给出了随机变量序列的强大数定律.本文利用胡舒合等获得的强大数定律,给出了随机变量序列的一些几乎必然收敛性,并给出了结果在PA,NA和两两NQD序列场合下的应用.     

强收敛与弱收敛的微小区别

对于独立同分布随机变量序列{Xn,n≥1},证明了其满足弱大数律的一个必要条件是:对于任何r∈(0,1),E|X|r<∞都成立.另外我们还举例说明了这样的条件不是充分的.     

多指标随机变量Marcinkiewica大数定律完全收敛性和收敛速度

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

两两NQD列的强大数定律

该文把同分布的两两NQD列的Kolmogorov强大数定律推广到了在一类广泛的条件下的不同分布的情形, 为此而建立的Kolmogorov Chung型强大数定律本身也是有意义的.      

次线性期望下大数定律的收敛速率（英文）

在本文中,我们研究次线性期望下独立同分布随机变量的大数定律的收敛速率.我们给出了大数定律的一个强L~p收敛版本和一个强拟必然收敛版本.     

随机变量序列的大数定律及常返性定理

对一类有界独立或相依的随机变量序列|ξn|,获得了它的伯努利大数定律、波雷尔强大数定律及常返性定理.作为应用,得出了Loève专著[1]中的推广的伯努利大数定律、常返性定理,改进了[1]中的推广的波雷尔强大数定律.     

两两NQD列的一个强大数定律

本文研究了一类广泛的随机变量序列两两NQD列的收敛性质，推广了与独立情形相类似的一些强大数定律，得到了新的结果．     

混合序列加权和的强大数定律

设{Xn,n≥1}是同分布随机变量序列,{αnk,n≥1,1≤k≤n}是满足某种条件的常数序列.本文在ψ-混合,ρ-混合,ρ~-混合条件下讨论了加权和∑kn=1ankXk的Kolmogorov强大数定律.     

两两NQD列的大数定律和完全收敛性

本文主要研究了两两NQD列的的大数定律和完全收敛性,获得了与独立情形一样的大数定律和完全收敛定理.     

关于两两NQD随机序列的一个极限定理

设为两两NQD随机序列, 且, 是一列严格单调递增的凸序列. 本文将 Feller (1946)关于独立同分布期望不存在随机序列的极限定理推广到两两NQD随机 序列的情形.     

Weak ergodicity of stationary pairwise independent processes

It is proven that a stationary process of pairwise independent random variables with values in a separable metric space is weakly ergodic, i.e. each random variable is independent of the system of invariant sets of the process. An example shows that a process of identically distributed pairwise independent random variables is in general, however, not weakly ergodic.

     

Almost sure convergence of sums of maxima and minima of positive random variables

For a sequence of independent and identically distributed positive random variables, the almost sure convergence of sums of maxima (when suitably normalized) to appropriate constants is proved for both bounded and unbounded random variables. A similar result is also proved for sums of minima of such variables.     

Some Limit Theorems for Sequences of Pairwise NQD Random Variables

In this article, the authors study some limit properties for sequences of pairwise NQD random variables, which are not necessarily identically distributed. They obtain Baum and Katz complete convergence and the strong stability of Jamison＇s weighted sums for pairwise NQD random variables, which may have different distributions. Some wellknown results are improved and extended.     

距离空间上的几乎处处中心极限定理

该文得到了关于一般可分距离空间上独立随机元序列的几乎处处中心极限定理(almost sure central limit theory, 简记为ASCLT). 作为应用, 该文给出了取值于可分Banach空间上随机元序列以及一类随机场序列满足ASCLT的充分条件,最后给出了关于多维随机变量序列极值的ASCLT.     

A note on a dependent risk model with constant interest rate

For a dependent risk model with constant interest rate, in which the claim sizes form a sequence of upper tail asymptotically independent and identically distributed random variables, and their inter-arrival times are another sequence of widely lower orthant dependent and identically distributed random variables, we will give an asymptotically equivalent formula for the finite-time ruin probability. The obtained asymptotics holds uniformly in an arbitrarily finite-time interval.     

On the convolution of Mittag–Leffler distributions and its applications to fractional point processes

We obtain the distribution of the sum of independent Mittag–Leffler (ML) random variables which are not necessarily identically distributed. Firstly we discuss the corresponding known result for independent and identically distributed ML random variables which follows as a special case of our result. Some applications of the obtained result to fractional point processes are also discussed.     

Strong law of large numbers for weighted sums

The law of large numbers for weighted sums of independent identically distributed random variables due to Chow and Lai is generalized to nonidentically distributed or pairwise independent summands. Supported by the Russian Foundation for Fundamental Research (grant No. 96-01-01920). Proceedings of the Seminar on Stability Problems for Stochastic, Models, Moscow, Russia, 1996, Part. II.     

负相伴样本平滑移动过程的完全收敛性

设{Y，Yi，-∞＜i＜∞}为一负相伴同分布随机变量序列，{ai，-∞＜i＜∞}绝对可和的实数序列，本文在适当的条件下，证明了平滑移动过程{∑k=1^n∑i=-∞^∞ai k Yi/n^1/t,n≥1}的完全收敛性．所得的结果改进了[1]中的定理1．