Complete Convergence for Weighted Sums of Negatively Superadditive Dependent Random Variables

Let $\{X_n,n\geq1\}$ be a sequence of negatively superadditive dependent (NSD, in short) random variables and $\{a_{nk}, 1\leq k\leq n, n\geq1\}$ be an array of real numbers. Under some suitable conditions, we present some results on complete convergence for weighted sums $\sum_{k=1}^na_{nk}X_k$ of NSD random variables by using the Rosenthal type inequality. The results obtained in the paper generalize some corresponding ones for independent random variables and negatively associated random variables.     

Convergence for Sums of Asymptotically Almost Negatively Associated Random Variables

In this paper, by applying the moment inequality for asymptotically almost negatively associated (AANA, in short) random sequence and truncated method, the equivalent conditions of complete moment convergence of the maximum partial for weighted sums of AANA random variables are obtained without assumptions of identical distribution, which generalize and improve the corresponding ones of{15},{16} and {17}, respectively.     

Convergence Rates of Wavelet Estimators in Semiparametric Regression Models Under NA Samples

Consider the following heteroscedastic semiparametric regression model:yi =XTiβ + g(ti) + σiei, 1 ＜ i ≤ n,where {Xi,1 ＜ i ＜ n} are random design points,errors {ei,1 ＜ i ＜ n} are negatively associated (...     

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??In this paper, by applying the moment inequality for asymptotically almost negatively associated (AANA, in short) random sequence and truncated method, the equivalent conditions of complete moment convergence of the maximum partial for weighted sums of AANA random variables are obtained without assumptions of identical distribution, which generalize and improve the corresponding ones of{15},{16} and {17}, respectively.     

Complete moment convergence for arrays of rowwise NSD random variables

In this paper, the complete convergence and complete moment convergence for arrays of rowwise negatively superadditive dependent (NSD, in short) random variables are investigated. Some sufficient conditions to prove the complete convergence and the complete moment convergence are presented. The results obtained in the paper generalize and improve some corresponding ones for independent random variables and negatively associated random variables.     

两两NQD阵列行和的若干极限定理

讨论了两两NQD阵列行和的弱收敛性、L_p收敛性和完全收敛性,在{X_(nk);1≤k≤k_n↑∞,n≥1}是Cesaro一致可积的相关条件下,获得了两两NQD阵列行和的弱收敛性、Lp收敛性和完全收敛性定理,将独立阵列行和的相关极限定理推广到了两两NQD阵列行和的情形.     

ND阵列加权乘积和的完全收敛性

设（Xni：1≤i≤n,n≥1）为行间ND阵列,g（x）是R^＋上指数为α的正则变化函数,{αni：1≤i≤n,n≥1}为满足条件max1≤i≤n｜ani｜=0（（g（n））^-1）的实数阵列．本文采用截尾的方法,得到了使ND随机变量阵列加权乘积和完全收敛的条件,并推广了以前学者的结论．     

NA列与两两NQD列的L^p收敛性

设{Xn,n≥1）是NA列或两两NQD列,{ank;1≤k≤n,n∈N）是实数阵列．利用矩不等式和截尾方法,研究了∑k=1^n ankXk的L^p收敛性,所获结论推广和改进了前人的相应结果．     

负相依样本平滑移动过程的完全收敛性

设{Yi;－∞＜i＜∞}为一负相伴的同分布随机变量序列，{ai；－∞＜i＜∞}为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 kYi/n^1/t;n≥1}的完全收敛性。     

独立重尾随机变量随机和的大偏差估计

本文研究了一类独立重尾随机变量随机和S（t)∧=∑k=1^N(t)Xk,t≥0的大偏差概率，其中{N(t),t≥0}是一放大晨负整数值随机变量；{Xn,n≥1}是非负，独立随机变量序列，并与{N(t),t≥０}独立。本文的结果将{Xn,n≥１}为独立同分布情形推广到了独立不同分布情形。     

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.     

行为ρ混合随机变量阵列的完全矩收敛性

本文假设 X ni ;i ≥1,n ≥{}1是一列行为？ρ混合随机变量阵列。在没有同分布和随机控制的假设条件下,作者讨论了？ρ混合随机变量的完全矩收敛性,所获得结果推广和改进了 Hu and Taylor （1997）,Zhu （2006）和 Wu and Zhu （2010）的相应定理。     

Equivalent Conditions of Complete qth Moment Convergence for Weighted Sums of Sequences of Negatively Orthant Dependent Random Variables

In this paper, the complete qth moment convergence for weighted sums of sequences of negatively orthant dependent random variables is investigated. By applying moment inequality and truncation methods, the equivalent conditions of complete qth moment convergence for weighted sums of sequences of negatively orthant dependent random variables are established. These results not only extend the corresponding results obtained by Li and Sp\v{a}taru\ucite{4}, Liang et al.\ucite{5}, Guo\ucite{6} and Gut\ucite{21} to sequences of negatively orthant dependent random variables, but also improve them.     

Complete convergence and almost sure convergence of weighted sums of random variables

Letr>1. For eachn1, let {X _nk , –<k<} be a sequence of independent real random variables. We provide some very relaxed conditions which will guarantee for every >0. This result is used to establish some results on complete convergence for weighted sums of independent random variables. The main idea is that we devise an effetive way of combining a certain maximal inequality of Hoffmann-Jørgensen and rates of convergence in the Weak Law of Large Numbers to establish results on complete convergence of weighted sums of independent random variables. New results as well as simple new proofs of known ones illustrate the usefulness of our method in this context. We show further that this approach can be used in the study of almost sure convergence for weighted sums of independent random variables. Convergence rates in the almost sure convergence of some summability methods ofiid random variables are also established.     

弱相依随机序列的若干新结果(英文)

设{Xn,n≥1}为-混合序列. 利用随机变量的截尾方法和-混合序列的三级数定理,讨论了-混合序列的收敛性质,并且得到了-混合序列的一类强极限定理,这些结果推广了独立序列的相应结果.最后研究了-混合序列加权和的强稳定性.     

NA序列重对数律的几个极限定理

设{X_n;n≥1}均值为零、方差有限的NA平稳序列。记S_n=∑_(k=1)~n X_k,M_n=maxk≤n|S_k|,n≥1.假设σ~2=EX_1~2+2∑_(k=2)~∞EX_1X_k>0。本文讨论了:当ε 0时,P{M_n≥εσ(2nloglogn)~(1/2)的一类加权级数的精确渐近性质,以及当ε∞时,P{M_n≤εσ(π~2n/(8loglogn))~(1/2)}的一类加权级数的精确渐近性质。这些性质与重对数律和Chung重对数律的速度有关。     

关于可列非齐次马氏链的若干极限定理

设{Xn,n≥0}是一列非齐次马尔科夫链,{an,n≥0}是一列固定的非负整数序列.首先构造了一个带参数的广义似然比函数,然后利用Borel-Cantelli引理证明随机变量序列几乎处处收敛性,得到了关于可列非齐次马氏链序偶广义平均的若干极限定理,推广了已有的结果.     

风险模型中重尾随机变量和的若干大偏差结果

进一步研究随机变量部分和与随机和的大偏差,其中S(n)=∑ni=1Xi,S(t)=∑N(t)i=1Xi(t>0).{Xn,n≥1}是一个独立同分布的随机变量(未必是非负的)序列具有共同的分布F(定义于R上)和有限期望μ=EX1.{N(t),t≥0}是一个非负的整数值的随机变量的更新计数过程且与{Xn,n≥1}相互独立.本文在假定F∈C条件下,进一步推广并改进了由Klüppelberg等和Kaiw等人给出的一些大偏差结果.这些结果可应用到某些金融保险方面的一些特定的问题中去.     

On the strong convergence properties for weighted sums of negatively orthant dependent random variables

In the paper, the strong convergence properties for two different weighted sums of negatively orthant dependent(NOD) random variables are investigated. Let {X_n, n ≥ 1}be a sequence of NOD random variables. The results obtained in the paper generalize the corresponding ones for i.i.d. random variables and identically distributed NA random variables to the case of NOD random variables, which are stochastically dominated by a random variable X. As a byproduct, the Marcinkiewicz-Zygmund type strong law of large numbers for NOD random variables is also obtained.     

强混合序列部分和的强逼近

具有 φ（ｘ）一致可积的混合序列的强逼近问题（其中 φ（ｘ）／ｘ２＋ δ↑∞,δ＞ ０）是本文所要论述的主题．文章给出的结论弥补了［１］中强混合序列的强逼近与独立序列之间的空隙,同时推广了［１］中的结论