功率和的强逼近

设$\{\xi_n, n\geq 1\}$是正的随机变量序列, $\ep \xi_1=\theta>0$, 设$S_n = \sum\limits_{i=1}^n \xi_i, Y_n=n\theta\log (S_n/(n\theta))$. 在该文中, 当$\{\xi_n\}$是独立同分布或强平稳$\varphi$ -混合的正随机变量序列时,作者给出功率和$\{Y_n\}$用Wiener过程的强逼近结果.     

线性过程的强逼近和重对数律

本文讨论由独立同分布随机变量列产生的线性过程的泛函型重对数律和强逼近, 同时又给出由NA随机变量列产生的线性过程的重对数律.     

高维Wiener sausage的强逼近

本文研究了四维及四维以上的Wiener sausage 的体积, 得到它们可以由一维Brown 运动强逼近. 作为应用, 推出了弱收敛和重对数率.     

随机右删失下回归函数的正态逼近

在随机右删失下，我们考虑了模型Yi=g(xi) εi，1≤i≤n．当εi's不服从正态分布时。我们构造了g(x)的估计式gn^(x)，并且利用强逼近的结论，导出了gn^(x)-Eg^n(x)的正态逼近结果，得到了较快的收敛速度。     

一类Markov链的强逼近

本文给出了一类非时齐的Markov链的强逼近.作为应用,建立了临床试验中Markov链自适应设计的强相合性,重对数律和弱收敛.     

一类Markov链的强逼近

本文给出了一类非时齐的Markov链的强逼近.作为应用,建立了临床试验中Markov链自适应设计的强相合性,重对数律和弱收敛.     

次序统计量线性函数的强逼近

设X_(n1)X_(n2)…X_(nk)为iid随机变量X_1…X_n的次序统计量,X_(ni)对C_(ni)的一种特殊情况,在一定的条件下,我们证明了T_n几乎处处收敛于一正态序列,并得到了其收敛速度     

线性空间中的共逼近

本文研究了在局部凸空间和赋范线性空间中的（f-）共逼近和强（f-）共逼近的一些性质,给出了f-共逼近、强f-共逼近和强f-Kolmogorov集的特征定理.并举例说明G.S.Rao的两主要定理是不正确的,同时作了相应的更正.所得的结果中的部分推广和改进了Song、Rao和Narang等人的相应结果.     

强多重线性积分

引入基于强测度论的强多重线性积分系统,属于多元向量值测度理论,其中的积分模型包含了几乎所有广泛应用的强积分。     

Approximations for Multi-Class Departure Processes

The exact analysis of a network of queues with multiple products is, in general, prohibited because of the non-renewal structure of the arrival and departure processes. Two-moment approximations (decomposition methods, Whitt [9] ) have been successfully used to study these systems. The performance of these methods, however, strongly depends on the quality of the approximations used to compute the squared coefficient variation (CV) of the different streams of products.In this paper, an approximation method for computing the squared coefficient of variation of the departure stream from a multi-class queueing system is presented. In particular, we generalize the results of Bitran and Tirupati [3] and Whitt [11] related to the interference effect.     

Linear Programming Approximations for Markov Control Processes in Metric Spaces

We develop a general framework to analyze the convergence of linear-programming approximations for Markov control processes in metric spaces. The approximations are based on aggregation and relaxation of constraints, as well as inner approximations of the decision variables. In particular, conditions are given under which the control problems optimal value can be approximated by a sequence of finite-dimensional linear programs.     

线性模型参数M估计的强相合性

本文研究线性模型中回归参数　Ｍ　估计的强相合性，　给出一些较弱的充分条件．　与陈希孺和赵林城的专著［１］中相应的结论比较，　这里给出的条件对矩的要求有较大的实质性改进．     

误差为NOD线性模型参数M估计的强相合性

研究了随机误差为NOD序列的线性模型中回归参数β_0的M估计,在较弱的矩条件下证明了回归参数M估计的强相合性,推广和改进了陈和赵(1996),杨(2002)和Wu(2006)等的结果,推广了肖(2007)等的结果.     

Strong Approximations of Martingale Vectors and Their Applications in Markov-Chain Adaptive Designs

The strong approximations of a class of R~d-valued martingales are considered.The conditions usedin this paper are easier to check than those used in [3] and [9].As an application,the strong approximation ofa class of non-homogenous Markov chains is established,and the asymptotic properties are established for themulti-treatment Markov chain adaptive designs in clinical trials.     

Strong Convergence of Euler Approximations of Stochastic Differential Equations with Delay Under Local Lipschitz Condition

The strong convergence of Euler approximations of stochastic delay differential equations is proved under general conditions. The assumptions on drift and diffusion coefficients have been relaxed to include polynomial growth and only continuity in the arguments corresponding to delays. Furthermore, the rate of convergence is obtained under one-sided and polynomial Lipschitz conditions. Finally, our findings are demonstrated with the help of numerical simulations.     

线性调控分枝过程的渐近增长

本文讨论了线性调控分枝过程的增殖速度和极限分布.由于GaltonWatson过程是它的特殊情况,故本文推广了[4]、[5]中的有关结果.     

马氏过程函数的强大数定律

本文主要研究了马氏过程函数以及马氏环境中马氏链函数的强大数定律．