无界域上1-集弱压缩算子的新不动点结果

本文证明Banach空间中无界域上一类弱序列连续和1-集弱压缩算子的若干新不动点定理.我们引入原点处弱半闭算子,得到该算子的若干不动点定理.进而将著名的Leray-Schauder不动点定理、Altman定理、Roth定理和Petryshyn定理推广到弱序列连续算子和1-集弱压缩算子以及原点处弱半闭算子的情形.本文的主要结果依赖于非紧性弱原子测度的有关条件.     

关于广义梯度＊弱上半连续的注记

在本文中，我们建立了集值映射的拟闭性和序列拟闭性的概念。在讨论巴拿赫空间上局部Ｌｉｐｓｃｈｉｔｚ函数的广义梯度的性质时，得到了集值映射ｘ→δｆ（ｘ）的＊弱上半连续与＊弱拟闭性等价的结果。并通过例子说明了＊弱拟闭性弱于ｘ弱序列拟闭性，从而改正了文（１）中的某些错误。     

弱Hopf代数上的Militaru-Stefan提升定理

本文研究了弱Hopf-Galois扩张的扩张模.利用忠实平坦的弱Hopf-Galois扩张理论,研究了弱Hopf代数上的Militaru-Stefan提升定理,推广了文献[10]中的相应结果.进一步地,通过诱导模的自同态环的cleft扩张刻画了弱稳定模.     

弱H-模代数上的弱Galois扩张

本文研究了弱模代数上的弱Galois扩张问题,利用不变子函子与积分方法,获得了弱Galois扩张的一个充分必要条件,推广了Cohen,Fishman和Montgomery的对应结果.     

临界可压缩模类与弱Jacobson根

本文讨论临界可压缩模类和结合环的弱Ｊａｃｏｂｓｏｎ根．首先，我们证明了非平凡的临界可压缩模类是素模的特殊类．其次，我们引入结合环的弱Ｊａｃｏｂｓｏｎ根．弱Ｊａｏｎｂｓｏｎ根是特殊根．最后，我们给出有关弱本原环，半弱本原环和弱Ｊａｃｏｂｓｏｎ根环的某些性质．     

广义弱Armendariz环及其扩张（英文）

对环R的一个自同态α,通过引入α-弱Armendariz环和α-弱拟Armendariz环研究了R相对于α的弱Armendariz性质.这两类环是对弱Armendariz环和弱拟Armendariz环的进一步推广,为研究环的弱Armendariz性质提供了新思路.本文对这两类环给出了一些刻画,构造了一些所需的例子和反例,统一和推广了一些已知的研究结果.     

关于弱交换PO—半群

在本文中我们引入弱交换ＰＯ－半群的概念，并研究这类半群到其Ａｒｃｈｉｍｅｄｅｓ子半群的半格分解，给出这类半群似于无序半群的相应结果的一个刻画。作为推论，我们得到弱交换ＰＯｅ－群和无序半群的相应刻画。     

弱Hopf代数的扭曲理论

本文研究了弱Hopf代数的扭曲理论的对偶问题.利用了弱Hopf代数上的弱Hopf双模的(辫子)张量范畴与扭曲弱Hopf代数上的弱Hopf双模的(辫子)张量范畴等价方法,得到Long模范畴是Yetter-Drinfel'd模范畴的辫子张量子范畴.推广了Oeckl(2000)的结果.     

弱拓扑下折非线性随机积分和微分方程组的解

在本文中，我们首先对具有随机定义域的弱连续随机算子组证明了一个Ｄａｒｂｏ型随机不动点定理。利用这一定理，我们对Ｂａｎａｃｈ空间中关于弱拓拟的非线性随机Ｖｏｌｔｅｒｒａ积分方程组给出了随机解的存在性准则，作为应用，我们得到了非线性随机微分方程的Ｃａｕｃｈｙ问题弱随机解的存在定理，也得到了这些随机方程在Ｂａｎａｃｈ空间中关于弱拓扑的极值随机解的存在性和随机比较结果。     

弱内积空间及其应用

把欧氏空间的内积条件中的恒正性去掉,在一般的线性空间中引入弱内积,使之成为弱内积空间,再引入弱正交、弱正交补概念．证明了任何数域上的线性空间都是弱内积空间、任何弱内积空间的子空间都有唯一的弱正交补;讨论这些结论在线性方程组中的应用,给出齐次线性方程组同解的一个充分必要条件．     

Approximation of intermediate quantile processes

We deal with quantile processes based on intermediate order statistics. Using an approximation of the uniform quantile process in weighted metrics, we prove weak convergence of weighted and nonweighted intermediate quantile processes.     

A modified functional delta method and its application to the estimation of risk functionals

The classical functional delta method (FDM) provides a convenient tool for deriving the asymptotic distribution of statistical functionals from the weak convergence of the respective empirical processes. However, for many interesting functionals depending on the tails of the underlying distribution this FDM cannot be applied since the method typically relies on Hadamard differentiability w.r.t. the uniform sup-norm. In this article, we present a version of the FDM which is suitable also for nonuniform sup-norms, with the outcome that the range of application of the FDM enlarges essentially. On one hand, our FDM, which we shall call the modified FDM, works for functionals that are “differentiable” in a weaker sense than Hadamard differentiability. On the other hand, it requires weak convergence of the empirical process w.r.t. a nonuniform sup-norm. The latter is not problematic since there exist strong respective results on weighted empirical processes obtained by Shorack and Wellner (1986) [25], Shao and Yu (1996) [23], Wu (2008) [32], and others. We illustrate the gain of the modified FDM by deriving the asymptotic distribution of plug-in estimates of popular risk measures that cannot be treated with the classical FDM.     

基于负相协样本的经验过程的弱收敛

我们借助于一个Ｒｏｓｅｒｎｔｈａｌ型不等式建立了基于负相协样本经验过程的弱收敛性,同时在证明过程中我们给出了负相协随机变量的几个有用的矩不等式。     

Empirical distributions in marked point processes

We study the asymptotic behaviour of the empirical distribution function derived from a stationary marked point process when a convex sampling window is expanding without bounds in all directions. We consider a random field model which assumes that the marks and the points are independent and admits dependencies between the marks. The main result is the weak convergence of the empirical process under strong mixing conditions on both independent components of the model. Applying an approximation principle weak convergence can be also shown for appropriately weighted empirical process defined from a stationary d-dimensional germ-grain process with dependent grains.     

Some properties of bivariate empirical hazard processes under random censoring

In Campbell (1982, IMS Lecture Notes—Monograph Series Vol. 2, pp. 243–256, IMS, Hayward, CA) and Campbell and Földes (1982, Proceedings, Internat. Colloq. Nonparametric Statist. Inform., 1980, North-Holland, New York) some asymptotic properties of bivariate empirical hazard processes under random censoring are given. Taking the representation of the empirical hazard process for bivariate randomly censored samples in Campbell, op. cit., as a starting point and restricting attention to strong properties, we obtain a speed of strong convergence for the weighted bivariate empirical hazard processes as well as a speed of strong uniform convergence for bivariate hazard rate estimators. Our approach is based on a local fluctuation inequality for the bivariate hazard process and differs from the martingale methods quite often used in the univariate case.     

Invariance principles for sums of Banach space valued random elements and empirical processes

Summary Almost sure and probability invariance principles are established for sums of independent not necessarily measurable random elements with values in a not necessarily separable Banach space. It is then shown that empirical processes readily fit into this general framework. Thus we bypass the problems of measurability and topology characteristic for the previous theory of weak convergence of empirical processes.Both authors were partially supported by NSF grants. This work was done while the second author was visiting the M.I.T. Mathematics Department     

WEAK CONVERGENCE OF EMPIRICAL PROCESSES OF SEQUENCES OF STATIONARY RANDOM VARIABLES

In this paper, the author improves Yoshihara''s result(J.Multivariate Anal. 8(1978),584-588) and proves the weak convergence of empirical processes for sequence of p-mixing strictly stationary random variable with $\[\rho (n) = O({n^{ - \frac{1}{2} - \theta }}),\theta > 0\]$. Moreover, the author simplifies the complex proof of weak convergence of empirical processes wwith random index and gets the corresponding result for $\[\alpha \]$-mixing stationary random variables.     

The proportional hazards regression model with staggered entries: A strong martingale approach

The proportional hazards regression model, when subjects enter the study in a staggered fashion, is studied. A strong martingale approach is used to model the two-time parameter counting processes. It is shown that well-known univariate results such as weak convergence and martingale inequalities can be extended to this two-dimensional model. Strong martingale theory is also used to prove weight convergence of a general weighted goodness-of-fit process and its weighted bootstrap counterpart.     

Weighted empirical processes in the nonparametric inference for Lévy processes

Given observations of a Lévy process, we provide nonparametric estimators of its Lévy tail and study the asymptotic properties of the corresponding weighted empirical processes. Within a special class of weight functions, we give necessary and sufficient conditions that ensure strong consistency and asymptotic normality of the weighted empirical processes, provided that complete information on the jumps is available. To cope with infinite activity processes, we depart from this assumption and analyze the weighted empirical processes of a sampling scheme where small jumps are neglected. We establish a bootstrap principle and provide a simulation study for some prominent Lévy processes.     

Weighted Invariance Principle for Banach Space-Valued Random Variables

A weighted weak invariance principle for nonseparable Banach space-valued functions is described via asymptotic behavior of a weighted Wiener process. It is proved that, unlike the usual weak invariance principle, the weighted variant cannot be characterized via validity of a central limit theorem in a Banach space. A strong invariance principle is introduced in the present context and used to prove the weighted weak invariance principle that we seek herewith. The result then is applied to empirical processes.