无界函数指标集上经验过程大偏差的局部概率不等式及应用

提出并研究了无界函数指标集合上经验过程的大偏差尾部的局部概率指数不等式, 给出了一种新的截割原始概率空间的方法和新的对称化方法. 利用这些方法, 导出了无界函数指标集合上非i.i.d.独立样本的经验过程大偏差尾部的局部概率指数不等式, 并给出了它们的若干应用. 作为应用的一个附带结果, 在Kolmogorov定理所给的条件下, 将Kolmogorov关于非i.i.d的独立随机变量和的强收敛结果推广到无界函数指标集上的经验过程情形, 并且得到了无界函数指标集上经验过程大偏差尾部的局部概率指数不等式和对数律.     

马氏环境中马氏链的一个概率不等式及其应用

本文研究了马氏环境中马氏链构成的随机变量之和的概率不等式问题.利用了结尾的方法,获得了马氏环境中马氏链构成的随机变量之和的尾部概率不等式,作为结果的应用,给出了将过程限制在(S,S∩F,PS)上的强大数定律.文中提出的方法和结果对研究独立的随机变量之和的大样本性质是十分有用的.     

Lipschitz局部强增殖算子的非线性方程的解的迭代构造

本文研究p一致光滑Banach空间X中Ishikawa迭代法.设T:X→K是Lipschitz局部强增殖算子,方程T_x=f的解集sol(T)非空.我们证明了sol(T)是一个单点集且Ishikawa序列强收敛到方程T_x=f的唯一解.另行,当T是从X的非空凸子集K到X的Lipschitz局部伪压缩映像且T的不动点集F(T)非空时,我们证明了F(T)是一个单点集且Ishikawa序列强收敛到T的唯一不动点.我们的结果改进和推广了[4]与[5]的结果.     

无界域上的强非线性变分问题

文献[1]和[3]中讨论了有界域Ω(?)R~n 上的强非线性变分问题.本文试图把[1]和[3]的结果推广到无界域上去.在Ⅰ中,我们建立了无界域上空间 W~lL_p(φ,Ω)与(?)L_p(φ,(?)Ω)中的迹定理.在Ⅱ中,我们得到了一个无界域上的 Poincarè型的不等式,这种类型的不等式,即使对一般的 Sobolev 空间(?)_p~1(Ω)来说,似乎也是新的.应用Ⅰ和Ⅱ的结果,在Ⅲ中,我们讨论了空间(?)~1E_p(φ,Ω)中强非线性变分问题及其相应的欧拉方程的可解性.当然区域Ω(?)R~n 也可以是无界的.     

行内负相关随机变量组列的完全收敛性

运用NA随机变量的矩不等式以及邵启满给出的关于NA随机变量概率不等式,在NA的情况下给出了类似与Chen(2005),Sung(2005)关于行内独立随机变量完全收敛性的结论.同时在给出的条件比上述作者的结论条件更加弱.     

独立不同分布经验过程的大偏差原理

设（Xn) n≥1是取值于可测空间（E,B^A)的一串独立随机变量,考虑经验过程Ln(f)=1/n ∑i=1^nf(Xi),f属于某个有界函数集F。运用Talagrand-Ledoux偏差不等式,我们得到其大偏差估计的充分必要条件。最后推广到无界函数族情形。     

正则变换的一种群表示

经典力学中的哈密顿正则变换所涉及的4个母函数F₁(q,Q),F₂(q,P),F₃(p,P),F₄(p,Q)和4种正则变量q,p,Q,P之间所有的关系,可以由7个基本关系式经线性变换而得到,这些变换是勒让德变换,变换是由32个8×8的变换矩阵来实现的,而这32个矩阵以4:1的关系与具有8个群元的D₄点群同态。热力学中的4个状态函数G(P,T),H(P,S),U(V,S),F(V,T)和4个热力学变量P,V,T,S之间的变换关系恰好与正则变换关系相同。热力学状态方程是源于宏观测量的实验结果的概括,而哈密顿正则变换是经典力学的理论性总结,它们的群表示是相同的,即它们的数学结构是相同的, 这种共性表明热力学变换是一维哈密顿正则变换的实例。     

NA随机变量列的有界重对数律

通过建立NA随机变量最大部分和的一些概率指数不等式，给出了具有不同分布的NA随机变量列有界重对数律的一些结果，因此推广了由R．Wittmann建立的独立随机变量的相关结果。     

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本文给出了上期望空间中独立随机变量部分和的最大不等式、指数 不等式、Marcinkiewicz-Zygmund不等式. 并且应用指数不等式和Marcinkiewicz-Zygmund不等式 研究了随机变量部分和序列完备收敛的性质.     

C(Ω)空间上的光滑点刻画

该文主要研究了C(Ω)型空间上的光滑点(即峰值函数)的存在性和稠密性,其中Ω为紧Hausdorff空间.当Ω不可度量化时,给出了例子说明存在紧Hausdorff空间Ω_1使得C(Ω_1)空间上的光滑点在全空间稠密,并且给出了反方面的例子说明存在紧Hausdorff空间Ω_2使得C(Ω_2)空间上的光滑点为空集.最后给出了C(Ω)型空间上的光滑点稠密的充要条件.     

A local probability exponential inequality for the large deviation of an empirical process indexed by an unbounded class of functions and its application

A local probability exponential inequality for the tail of large deviation of an empirical process over an unbounded class of functions is proposed and studied. A new method of truncating the original probability space and a new symmetrization method are given. Using these methods, the local probability exponential inequalities for the tails of large deviations of empirical processes with non-i.i.d. independent samples over unbounded class of functions are established. Some applications of the inequalities are discussed. As an additional result of this paper, under the conditions of Kolmogorov theorem, the strong convergence results of Kolmogorov on sums of non-i.i.d. independent random variables are extended to the cases of empirical processes indexed by unbounded classes of functions, the local probability exponential inequalities and the laws of the logarithm for the empirical processes are obtained.     

Hoeffding’s Inequality for Sums of Dependent Random Variables

Let \(X_1,\ldots ,X_n\) be, possibly dependent, [0, 1]-valued random variables. What is a sharp upper bound on the probability that their sum is significantly larger than their mean? In the case of independent random variables, a fundamental tool for bounding such probabilities is devised by Wassily Hoeffding. In this paper, we provide a generalisation of Hoeffding’s theorem. We obtain an estimate on the aforementioned probability that is described in terms of the expectation, with respect to convex functions, of a random variable that concentrates mass on the set \(\{0,1,\ldots ,n\}\). Our main result yields concentration inequalities for several sums of dependent random variables such as sums of martingale difference sequences, sums of k-wise independent random variables, as well as for sums of arbitrary [0, 1]-valued random variables.     

The Convergence of the Sums of Independent Random Variables Under the Sub-linear Expectations

Let {Xn;n≥1} be a sequence of independent random variables on a probability space(Ω,F,P) and Sn=∑k=1n Xk.It is well-known that the almost sure convergence,the convergence in probability and the convergence in distribution of Sn are equivalent.In this paper,we prove similar results for the independent random variables under the sub-linear expectations,and give a group of sufficient and necessary conditions for these convergence.For proving the results,the Levy and Kolmogorov maximal inequalities for independent random variables under the sub-linear expectation are established.As an application of the maximal inequalities,the sufficient and necessary conditions for the central limit theorem of independent and identically distributed random variables are also obtained.     

On the convergence in law of almost all sums of independent random variables

The paper deals with sums of independent and identically distributed random variables defined on some probability space which are multiplied by random coefficients. These coefficients are the values of independent random variables defined on another probability space. We obtain conditions for the weak convergence of weighted sums, for almost all coefficients, to some infinitely divisible distribution. The limit distribution for these sums is found. Supported by the Russian Foundation for Fundamental Research (grant No. 93-011-16099). Proceedings of the Seminar on Stability Problems for Stochastic Models, Moscow, 1993.     

User-Friendly Tail Bounds for Sums of Random Matrices

This paper presents new probability inequalities for sums of independent, random, self-adjoint matrices. These results place simple and easily verifiable hypotheses on the summands, and they deliver strong conclusions about the large-deviation behavior of the maximum eigenvalue of the sum. Tail bounds for the norm of a sum of random rectangular matrices follow as an immediate corollary. The proof techniques also yield some information about matrix-valued martingales. In other words, this paper provides noncommutative generalizations of the classical bounds associated with the names Azuma, Bennett, Bernstein, Chernoff, Hoeffding, and McDiarmid. The matrix inequalities promise the same diversity of application, ease of use, and strength of conclusion that have made the scalar inequalities so valuable.     

Asymptotic estimate for sums of independent random variables in a Banach space

A number of estimates are established for the probability of large deviations of sums of independent random variables in a Banach space. In the onedimensional case inequalities of this type occur in the familiar inequalities of Nagaev and Fuk.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 43, No. 2, pp. 270–273, February, 1991.     

On the probabilistic analysis of an approximation algorithm for solving the <Emphasis Type="Italic">p</Emphasis>-median problem

In order to solve the location problem in the p-median form we present an approximation algorithm with time complexity O(n ₂) and the results of its probabilistic analysis. The input data are defined on a complete graph with distances between the vertices expressed by the independent random variables with the same uniform distribution. The value of the objective function produced by the algorithm amounts to a certain sum of the random variables that we analyze basing on an estimate for the probabilities of large deviations of these sums. We use a limit theorem in the form of the Petrov inequalities, taking into account the dependence of the random variables in the sum. The probabilistic analysis yields some estimates for the relative error and the failure probability of our algorithm, as well as conditions for its asymptotic exactness.