渐近负相关随机域强定律的收敛率

In this paper,a notion of negative side ρ-mixing (ρ--mixing) which can be regarded as asymptotic negative association is defined,and some Rosenthal type inequalities for ρ--mixing random fields are established. The complete convergence and almost sure summability on the convergence rates with respect to the strong law of large numbers are also discussed for ρ--mixing random fields. The results obtained extend those for negatively associated sequences and ρ*-mixing random fields.     

Inequalities of Maximum of Partial Sums and Convergence Rates in the Strong Laws for ρ~-mixing Random Variables

In this paper,we establish a Rosenthal-type inequality of partial sums for ρ~mixing random variables.As its applications,we get the complete convergence rates in the strong laws for ρ~-mixing random variables.The result obtained extends the corresponding result.     

Kolmogorov-Feller型弱大数律的一注记

In this paper, the Kolmogorov-Feller type weak law of large numbers are obtained,which extend and improve the related known works in the literature.     

$rho^*$-混合随机变量列加权和的完全收敛性

In this paper, the complete convergence of weighted sums for ρ*-mixing sequence of random variables is investigated. By applying moment inequality and truncation methods, the equivalent conditions of complete convergence of weighted sums for ρ*-mixing sequence of random variables are established. We not only promote and improve the results of Li et al. (J. Theoret. Probab., 1995, 8(1): 49-76) from i.i.d. to ρ*-mixing setting but also obtain their necessities and relax their conditions.     

弱负相关随机场的两个强收敛定理

证明了弱负相关的两个强收敛定理,推广和改进了Ｂｒｙｃ和Ｓｍｏｌｅｎｓｋｉ（１９９３）的结果。     

关于齐次树上随机场的一类强偏差定理

通过引进渐近对数似然比作为齐次树上任意Markov随机场逼近的一种度量,通过构造鞅的方法,建立了关于随机场的一类强偏差(也称小偏差)定理.所得结论推广了一个已知的结果.     

rho混合随机变量的强大数律

In this paper we present some results for the general strong laws of large numbers of ■-mixing random variables by a maximal inequality of Utev and Peligrad.These results extend and improve the related known works in the literature.     

B-值强混合随机场的Rosenthal型不等式及其应用

得到了关于B- 值ρ * -混合随机场和Φ *-混合随机场的形如Rosenthal不等式的一些矩不等式 ,并用之讨论了这些随机场的弱大数律、强大数律和完全收敛性.     

■距离及随机场的失真率函数

本文将距离的概念推广到了随机场情形,证明了该推广定义的合理性,并导出了一些上界和下届估计.特别地,我们证明了有相变情况下２维Ｉｓｉｎｇ模型的两个极端测度μ＋,μ－间的距离大于１／３．我们着重研究了距离和失真率函数之间的关系,得到了一些重要性质．这些性质可以用来证明编码定理．     

关于一致有界树上随机场的一类强偏差定理

通过引进渐近对数似然比作为一致有界树上任意随机场关于树指标Markov链逼近的一种度量,通过构造鞅的方法,建立了关于随机场的一类强偏差定理.推广了一些已知的结果.     

Convergence rates in the strong laws of asymptotically negatively associated random fields

In this paper, a notion of negative side p-mixing (p -mixing) which can be regardedas asymptotic negative association is defined, and some Rosenthal type inequalities for p -mix-ing random fields are established. The complete convergence and almost sure summability onthe convergence rates with respect to the strong law of large numbers are also discussed for p--mixing random fields. The results obtained extend those for negatively associated sequences andp“ -mixing random fields.     

Asymptotics for dependent Bernoulli random variables

This paper considers a sequence of Bernoulli random variables which are dependent in a way that the success probability of a trial conditional on the previous trials depends on the total number of successes achieved prior to the trial. The paper investigates almost sure behaviors for the sequence and proves the strong law of large numbers under weak conditions. For linear probability functions, the paper also obtains the strong law of large numbers, the central limit theorems and the law of the iterated logarithm, extending the results by James et al. (2008).     

Convergence rates in strong ergodicity for Markov processes

A coupling method is used to obtain the explicit upper and lower bounds for convergence rates in strong ergodicity for Markov processes. For one-dimensional diffusion processes and birth–death processes, these bounds are sharp in the sense that the upper one and the lower one only differ in a constant.     

A strong approximation for logarithmic averages of partial sums of random variables

LetS _n be the partial sums of -mixing stationary random variables and letf(x) be a real function. In this note we give sufficient conditions under which the logarithmic average off(S _n / _n ) converges almost surely to _– f(x)d(x). We also obtain strong approximation forH(n)= _k=1 ⁿ k ^–1 f(S _k /k)=logn _– f(x)d(x) which will imply the asymptotic normality ofH(n)/log^1/2 n. But for partial sums of i.i.d. random variables our results will be proved under weaker moment condition than assumed for -mixing random variables.     

On complete convergence for weighted sums of asymptotically linear negatively dependent random field

In this paper we establish the complete convergence for weighted sums of asymptotically linear negatively quadrant dependent random field, which contains a linear negatively quadrant dependent field and a ρ^∗${\rho }^{\ast }$-mixing random field.     

On Beveridge-Nelson decomposition and limit theorems for linear random fields

We consider linear random fields and show how an analogue of the Beveridge-Nelson decomposition can be applied to prove limit theorems for sums of such fields.     

On the Kolmogorov inequalities for quadratic forms of dependent uniformly bounded random variables

Some Kolmogorov probability inequalities for quadratic forms and weighted quadratic forms of negative superadditive dependent (NSD) uniformly bounded random variables are provided. Using these inequalities, some complete convergence of randomized quadratic forms under some suitable conditions are evaluated. Moreover, various examples are presented in which the given conditions of our results are satisfied.     

Almost sure behaviour ofF-valued random fields

Summary LetX _n, n ^d be a field of independent random variables taking values in a semi-normed measurable vector spaceF. For a broad class of fields _n, ^d of positive numbers, the almost sure behaviour of _knX_k/_n, n ^d is studied. The main result allows us to deduce some new and well-known theorems for fields of independentF random variables from related results for fields of independent real random variables.Supported in part by the Youth Science Foundation of China, No. 19001018Supported by the National Natural Science Foundation of China     

Convergence rates in the SLLN for some classes of dependent random fields

Let be a random field i.e. a family of random variables indexed by Nr, r?2. We discuss complete convergence and convergence rates under assumption on dependence structure of random fields in the case of nonidentical distributions. Results are obtained for negatively associated random fields, ρ^?-mixing random fields (having maximal coefficient of correlation strictly smaller then 1) and martingale random fields.