Complete convergence for (α,β)-mixing random variables sequences

The convergence properties are studied through the analysis of (α,β)-mixing random variables sequences in different situations. By using the corresponding inequality, a convergence theorem was presented, and some results for (α,β)-mixing random variables sequences with different distributions were obtained. The results extend the corresponding theorems of independent random variable sequences.     

Finite Time Ruin Probability with Variable Interest Rate and Extended Regular Variation

Consider an insurance risk model, in which the surplus process satisfies a recursive equation Un=Un-1(1 rn)-Xn for n≥1, where U0=x≥0 is the initial surplus, {rn；n≥1} the interest rate sequence, {Xn；n≥l} the sequence of i. i. d. real-valued random variables with common distribution function F, which denotes the gross loss during the nth year. We investigate the ruin probability within a finite time horizon and give the asymptotic result as x→∞.     

混合对数正态分布最大值的极限分布

设{Xn,n≥1}是独立同分布的随机变量序列,并且每个随机变量Xn服从混合对数正态分布.Mn=max{Xk,1≤k≤n}表示{Xn,n≥1}的部分最大值,同服从混合对数正态分布的独立随机变量最大值的极限分布以及相应的赋范常数.     

Rosenthal inequality for NOD sequences and its applications

Rosenthal inequality for NOD(negatively orthant dependent) random variable sequences is established.As its applications,two theorems of complete convergence of weighted sums for arrays of NOD random variables are given,which extend the corresponding known results.     

Strong Law of Large Numbers and Complete Convergence for Sequences of -Mixing Random Variables

We give some theorems of strong law of large numbers and complete convergence for sequences of -mixing random variables. In particular, Wittmann's strong law of large numbers and Teicher's strong law of large nnumbers for independent random variables are generalized to the case of -minxing random variables.     

B值随机序列的一类强极限定理

设{φn(x),n≥1}是具有φ-特征的偶函数序列,{Xn,n≥1)是B值随机变量序列,在φn(Xn)的矩条件下研究了随机序列{Xn,n≥1}所满足的强极限定理和强大数定律,使一些经典的强大数定律成为特例.     

ρ~*混合序列的Chover型重对数律

设{Xn,n≥1}是同分布的ρ*混合序列,其分布属于特征指数为α(0<α<2)的非退化稳定分布正则吸引场. 利用ρ*混合序列的矩不等式证明了依概率1有lim n→∞ sup((∑ni=1Xi)/n1/α)1/(log log n)=e1/α,并获得了一系列等价条件.     

Population-size-dependent branching processes in Markovian random environments

A branching model {Z n} n≥0is considered where the offspring distribution of the population's evolution is not only dependent on the population size, but also controlled by a Markovian environmental process {ξ n} n≥0. For this model, asymptotic behaviour is studied such as limn→∞Z n and limn→∞Z n/m n in the case that the mean m k, θof the offspring distribution converges to m>1 as the population size k grows to ∞. In the case that {ξ n} n≥0is an irreducible positive recurrent Markov chain, certain extinction (i.e. P(Z n=0 for some n)=1) and noncertain extinction (i.e. P(Z n=0 for some n)<1) are studied.     

矩条件下NA随机变量的强极限定理

令{X,Xn,n≥1}为同分布的NA随机变量序列,{an,n≥1}是一正常数序列且an/n↑.讨论了{X,Xn,n≥1}的强大数定律和完全收敛性,得到了与条件∞∑n=1P(|X|an)∞等价的结果.另外,该结果推广了关于两两独立同分布序列的相应结果.     

Strong Law of Large Numbers and Complete Convergence for Sequences of φ-Mixing Random Var, ables

We give some theorems of strong law of large numbers and complete convergence for sequences of φ-mixing random variables. In particular, Wittmann＇s strong law of large numbers and Teicher＇s strong law of large nnumbers for independent random variables are generalized to the case of φ -minxing random variables.     

(α,β)混合序列的强极限定理

研究了(α,β)混合序列的收敛定理并得到(α,β)混合序列加权和的强稳定性.并且还得到一些新的大数定理.这些结果推广了独立序列的相应结果.     

P^＊-混合序列重对数律的收敛速度

利用截尾、矩不等式、矩和级数收敛的关系、Borel—Cantelli引理等研究了P^＊-混合序列{X,,i∈Z^d＋}重对数律的收敛速度,在较弱的矩条件下得到了与独立同分布（i．i．d）实值随机变量序列类似的结果,同时给出了P^＊-混合序列重对数律收敛速度的一种描述．     

ρ~-混合序列完全矩收敛的精确渐近性

设{X_n,n≥1}是一列严平稳的ρ~-混合随机变量序列,利用ρ~-混合随机变量序列的中心极限定理和矩不等式等,在适当的条件下给出ρ~-混合随机变量序列部分和在一般对数律下的完全矩收敛精确渐近性的一般函数式.     

ρ混合序列线性形式的强稳定性

研究ρ混合序列线性形式的强稳定性,得到ρ混合序列具有线性形式强稳定性的充分条件．     

S_p系统的强收敛性

讨论了一定条件下的φ-混合、ψ-混合随机变量序列是Sp系统,通过建立φ-混合、ψ-混合序列的矩不等式,研究了Sp系统的强收敛性,将独立同分布随机变量的Marcinkiewicz强大数定律推广到了ψ-混合、φ-混合序列.     

非同分布φ混合序列加权和的强极限收敛性

利用φ混合序列矩不等式和截尾的处理方法,研究非同分布φ混合序列加权和强极限收敛性质的问题,得到了若干新结果,推广并改进了独立同分布情形下的相应结果.     

混合随机阵列加权和的若干收敛性质

在{ank, 1≤k≤kn, n≥1}一致可积的条件
下, 利用〖AKρ~D〗混合、 〖AKφ~D〗混合序列矩不等式和截尾法, 证明了〖AKρ~D〗混
合、 〖AKφ~D〗混合阵列行加权和最大值max〖DD(〗〖〗1≤j≤kn〖DD)〗〖J
B((〗∑〖DD(〗j〖〗k=1〖DD)〗ankXnk-E∑〖DD(〗j〖〗k=1〖DD)〗ank
Xnk〖JB))〗的弱收敛、 Lr收敛和完全收敛性.     

同分布ψ-混合随机变量线性形式的强稳定性

研究了同分布啦混合随机变量线性形式的强稳定性,通过对随机变量运用截尾术得到了同分布ψ-混合随机变量具有强稳定性的充分条件,此结果在某种程度上推广了独立随机变量的相应定理。     

α-混合随机变量序列线性形式的强稳定性

研究了α-混合随机变量序列线性形式的强稳定性.利用随机变量的截尾术及强大数定律,得到了不同分布α-混合随机变量序列具有线性形式强稳定性的充分条件,在某种程度上推广了独立随机变量的线性形式的强稳定性的相应结果.