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。     

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。     

E—值独立随机变量部分和大偏差及应用

本文给出了Ｅ－值随机变量之部分和大偏差定理（Ｅ是一类局部凸空间）。作为其应用，解决了独立弱收敛随机变量列的经验分布的大偏差问题，从而推广了Ｄｏｎｓｋｅｒ－Ｖａｒａｄｈａｎ的结果。     

关于■(0<2)和■的矩

设{X,X_(?);(?)∈N~d 是 d-维指标实值独立同分布随机变量序列,S(?)=sum from k≤(?),(?)∈N~d.本文的目的是要研究仅在条件 E(|X|~r(L|X|))<+∞(如果1<γ<2,附加 EX=0)下,sup(?)|S_(?)|/|(?)~(L/r)(0<γ<2)的矩问题,和仅在条件 EX=0,EX~2<+∞和 E(X~2(L|X|)~(d-1)/L_2|X|)<+∞下,(?)|S_(?)|/(|(?)|L_2|(?)|)~(1/2)的矩问题.这两个问题在本文中获得完满的解决.     

NA序列部分和的完全性收敛性

本文讨论了非平稳ＮＡ随机变量序列部分和的完全收敛性,获得了一般形式的完全收敛速度与矩条件之间的等价关系,其结果与独立情形一致,从而证实了ＮＡ序列与独立序列有着极为类似的完全收敛性。     

NA及B-值随机变量序列的平均移动过程的大偏差原理

本文在比较一般的条件下建立了两个大偏差原理：平稳NA随机变量序列的平均移动过程的大偏差原理和独立同分布的B-值随机变量序列的平均移动过程的大偏差原理。     

平稳NA序列的中偏差原理

设｛Ｘｎ;ｎ≥１｝是平稳ＮＡ序列。假设Ｘ１具有有限的指数阶矩及其它适当条件,本文利用Ｃｒａｍｅｒ方法及分块技术,证明了｛Ｘｎ;ｎ≥１｝的部分和序列满足中偏差原理。     

Moments of the maximum of normed partial sums of ρ^--mixing random variables

Let {Xn,n ≥ 1} be a sequence of identically distributed ρ^--mixing random variables and set Sn =∑i^n=1 Xi,n ≥ 1,the suffcient and necessary conditions for the existence of moments of supn≥1 ｜Sn/n^1/r｜^p（0 〈 r 〈 2,p 〉 0） are given,which are the same as that in the independent case.     

An approximate maximum likelihood estimation for non-Gaussian non-minimum phase moving average processes

An approximate maximum likelihood procedure is proposed for the estimation of parameters in possibly nonminimum phase (noninvertible) moving average processes driven by independent and identically distributed non-Gaussian noise. Under appropriate conditions, parameter estimates that are solutions of likelihood-like equations are consistent and are asymptotically normal. A simulation study for MA(2) processes illustrates the estimation procedure.     

Complete convergence for moving average processes associated to heavy-tailed distributions and applications

The complete convergence is obtained for the moving average processes associated to heavy-tailed distributions via integral test. As the applications, two versions of Chover's law of the iterated logarithm are deduced.     

Bootstrap in moving average models

We prove that the bootstrap principle works very well in moving average models, when the parameters satisfy the invertibility condition, by showing that the bootstrap approximation of the distribution of the parameter estimates is accurate to the ordero(n ^−1/2) a.s. Some simulation studies are also reported.     

Convergence rates of tail probabilities for sums under dependence assumptions

Let {X,X1,X2,……}be a zero mean strictly stationary Ф-mixing sequence. Set Sn=∑n k=1 and f（x^p）=∑∞n=1 n^r-2P（｜Sn｜≥x^p√ES2nlog n）,When ε〉（√2）1/p,for p〉1/2 and r〉1,the conditions for ∫∞ε f（x^p）dx 〈∞ to hold is established, by using coupled methods together withstrong approximation, which are different from the traditional symmetrization and Hoffman-JФrgensen inequality.     

On prediction of integrated moving average processes

We shall consider the asymptotic properties of predictors with estimated coefficients for IMA processes and how to determine the order of predictors to minimize the error of prediction. For this purpose, the effect of the initial values on predictors is also considered.     

Precise asymptotics of complete moment convergence on moving average

Let {ξi,-∞i∞} be a doubly infinite sequence of identically distributed-mixing random variables with zero means and finite variances,{ai,-∞i∞} be an absolutely summable sequence of real numbers and X k =∑i=-∞+∞ aiξi+k be a moving average process.Under some proper moment conditions,the precise asymptotics are established for     

Convergence rates for probabilities of moderate deviations for moving average processes

The present paper first shows that, without any dependent structure assumptions for a sequence of random variables, the refined results of the complete convergence for the sequence is equivalent to the corresponding complete moment convergence of the sequence. Then this paper investigates the convergence rates and refined convergence rates （or complete moment convergence） for probabilities of moderate deviations of moving average processes. The results in this paper extend and generalize some well-known results.     

Determination of the probability of ultimate ruin by maximum entropy applied to fractional moments

In this work we present two different numerical methods to determine the probability of ultimate ruin as a function of the initial surplus. Both methods use moments obtained from the Pollaczek–Kinchine identity for the Laplace transform of the probability of ultimate ruin. One method uses fractional moments combined with the maximum entropy method and the other is a probabilistic approach that uses integer moments directly to approximate the density.     

Stable limits of empirical processes of moving averages with infinite variance

The paper obtains a functional limit theorem for the empirical process of a stationary moving average process X_t with i.i.d. innovations belonging to the domain of attraction of a symmetric -stable law, 1<<2, with weights b_j decaying as j^−β, 1<β<2/. We show that the empirical process (normalized by N^1/β) weakly converges, as the sample size N increases, to the process c_x⁺L⁺+c_x⁻L⁻, where L⁺,L⁻ are independent totally skewed β-stable random variables, and c_x⁺,c_x⁻ are some deterministic functions. We also show that, for any bounded function H, the weak limit of suitably normalized partial sums of H(X_s) is an β-stable Lévy process with independent increments. This limiting behavior is quite different from the behavior of the corresponding empirical processes in the parameter regions 1/<β<1 and 2/<β studied in Koul and Surgailis (Stochastic Process. Appl. 91 (2001) 309) and Hsing (Ann. Probab. 27 (1999) 1579), respectively.