非Lipschitz条件下g 上鞅的非线性Doob Meyer分解

作者讨论非Lipschitz条件下g 上鞅的非线性Doob Meyer 分解.　为此讨论一类漂移系数g(s,·,·)关于(y,z)不满足Lipschitz条 件的倒向随机微分方程解的存在唯一性,运用Biharis不等式证明了一类倒向随机微分方程的比较定理以及g 上解的极限定理.     

一般随机Dirichlet级数所表示的整函数

该文研究了一般随机Dirichlet级数的所表示整函数增长性和值分布，得出了重要结论：在适当条件下，任何水平带形上或水平线上增长级与全面上相同，对于ρ随机Dirichlet级数(0＜ρ＜∞)a.s.在任何宽为〖SX(〗π[]ρ〖SX)〗的水平带形内，至少有一条ρ级没有有穷例外值的Borel线。     

广义系统的Hamilton矩阵与H\-2代数Riccati方程的稳定化解

研究线性连续广义系统的Hamilton矩阵及H\-2代数Riccati方程.　提出一个标准的广义H\-2代数Riccati方程及对应的Hamilton矩阵，给出该Hamilton矩阵的几个重要性质. 在此基础上，得到该广义H\-2代数Riccati方程的稳定化解存在的一个充分条件并给出求解方法.此条件具有一般性, 主要定理是正常系统相应结果的推广.     

一类非齐次A 调和方程组很弱解的性质

该文应用Hodge分解定理，得到了非齐次A 调和方程组 -D\-i(A\+\{ij\}(x,Du))+D\-if\+i\-j(x)=0, j=1, \:, m的很弱解是弱解，进一步，利用Morrey空间法与Campanato空间法以及齐次化方法，作者得出了该方程的很弱解是局部H[AKo¨D]lder连续的，并且得出了H[AKo¨D]lder连续指数μ与λ之间的多值函数关系式。     

非线性Φ 伪压缩映象不动点的具误差的迭代过程

该文的目的是要引入比重要的 强伪压缩映象更一般的Φ 伪压缩映象，并且在更一般的假设下，用具误差的 Ishikawa和 Mann迭代过程去研究这类映象不动点的迭代逼近问题。研究结果表明：Φ 伪压缩映象T的一致连续性保证了在任意实Banach空间E中，Ishikawa迭代序列强收敛于T的唯一不动点；进一步，如果E是一致光滑的则T的连续性是不必要的     

广义共同逼近问题的适定性

设C是实Banach空间X中有界闭凸子集且0是C的内点，G是X中非空闭的有界相对弱紧子集.记K(X)为X的非空紧凸子集全体并赋Hausdorff距离，KG(X)为集合｛A∈K(X)；A∩G=｝的闭包.称广义共同逼近问题minC(A,G)是适定的是指它有唯一解(x0,z0)，且它的每个极小化序列均强收敛到(x0,z0).在C是严格凸和Kadec的假定下，证明了｛A∈K(X)；minC(A，G)是适定的｝含有KG(X)中稠Gδ子集，这本质地推广和延拓了包括De Blasi,Myjak and Papini［1］、Li［2］和De Blasi and Myjak［3］等人在内的近期相应结果.     

广义α-stable过程的确切Hausdorff测度函

该文证明了广义α stable过程的图集和像集的确切Hausdorff测度函数。该结果推广了N指标d维α stable过程和N指标d维广义Brownian sheet的相应结果。     

图的生成树, 基本圈与Betti亏数

G为图且T是G的一棵生成树. 记号ξ(G, T)表示G＼E(T)中边数为奇数的连通分支个数. 文献［2］称ξ(G)=min[DD(X]T[DD)]ξ(G, T)为图G的Betti亏数, 这里min取遍G的所有生成树T. 由文献［2］知, 确定一个图G的最大亏格主要确定这个图的Betii亏数ξ(G).该文研究与Betti亏数有关的图的特征结构, 得到了关于图的最大亏格的若干结果.     

凸规划的极大熵函数序列及其收敛性

为了消除凸规划问题中极大熵方法所导致的数值病态，该文应用Lagrange乘子法及赋范原理，给出一类凸规划问题的极大熵函数序列，并证明该序列一致收敛于凸规划的最优解。     

Sasakian流形中反不变极小子流形的稳定性

本文给出了Ｓａｓａｋｉａｎ流形中反不变极小子流形是稳定或不稳定的一个充分条件．     

Saddlepoint approximations to studentized bootstrap distributions based onM-estimates

Summary  Saddlepoint methods can provide extremely accurate approximations to resampling distributions. This article applies them to distributions of studentized bootstrap statistics based on robustM-estimates. As examples we consider the studentized versions of Huber’sM-estimate of location, of its initially MAD scaled version, and of Huber’s proposal 2. The studentized version of Huber’s proposal 2 seems to be a preferable measure of location. Remarks on implementation and related problems are given.     

Cornish-Fisher expansions using sample cumulants and monotonic transformations

General formulas of the asymptotic cumulants of a studentized parameter estimator are given up to the fourth order with the added higher-order asymptotic variance. Using the sample counterparts of the asymptotic cumulants, formulas for the Cornish-Fisher expansions with third-order accuracy are obtained. Some new methods of monotonic transformations of the studentized estimator are presented. In addition, similar transformations of a fixed normal deviate are proposed up to the same order with some asymptotic comparisons to the transformations of the studentized estimator. Applications to a mean and a binomial proportion are shown with simulations for estimation of the proportion.     

EDGEWORTH EXPANSION AND BOOTSTRAP APPROXIMATION FOR THE STUDENTIZED MLE FROM RANDOMLY CENSORED EXPONENTIAL SAMPLES

In this paper,the author studies the asymptotic accuracies of the one-term Edgeworth expansions and the bootstrap approximation for the studentized MLE from randomly censored exponential population.It is shown that the Edgeworth expansions and the bootstrap approximation are asymptotically close to the exact distribution of the studentized MLE with a rate.     

Bahadur efficiency and robustness of studentized score tests

We derive the exact Bahadur slopes of studentized score tests for a simple null hypothesis in a one-parameter family of distributions. The Student's t-test is included as a special case for which a recent result of Rukhin (1993, Sankhy Ser. A, 55, 159–163) was improved upon. It is shown that locally optimal Bahadur efficiency for one-sample location models with a known or estimated scale parameter is attained within the class of studentized score tests. The studentized test has an asymptotic null distribution free of the scale parameter, and the optimality of likelihood scores does not depend on the existence of a moment generating function. We also consider the influence function and breakdown point of such tests as part of our robustness investigation. The influence of any studentized score test is bounded from above, indicating certain degree of robustness of validity, but a bounded score function is needed to cap the influence from below and to ensure a high power breakdown point. We find that the standard Huber-type score tests are not only locally minimax in Bahadur efficiency, but also very competitive in global efficiency at a variety of location models.     

Saddlepoint approximations for studentized compound Poisson sums with no moment conditions in audit sampling

Saddlepoint approximations for the studentized compound Poisson sums with no moment conditions in audit sampling are derived. This result not only provides a very accurate approximation for studentized compound Poisson sums, but also can be applied much more widely in statistical inference of the error amount in an audit population of accounts to check the validity of financial statements of a firm. Some numerical illustrations and comparison with the normal approximation method are presented.     

On the joint distribution of studentized order statistics

In this paper, the joint distribution of some special linear combinations of the (internally) studentized order statistics are derived for both normal and exponential populations; the exact relationship between their pdf's is also obtained. The exact sampling distributions of studentized extreme deviation statistic, which has been proposed by Pearson and Chandra Sekar (1936,Biometrika,28, 308–320), are derived for these two populations. An application to the most powerful location and scale invariant test is discussed briefly.     

A Berry-Esséen bound for student's statistic in the non-I.I.D. case

We establish a Berry-Esséen bound for Student's statistic for independent (nonidentically) distributed random variables. In particular, the bound implies a sharp estimate similar to the classical Berry-Esséen bound. In the i.i.d. case it yields sufficient conditions for the Central Limit Theorem for studentized sums. For non-i.i.d. random variables the bound shows that the Lindeberg condition is sufficient for the Central Limit Theorem for studentized sums.Research supported by the SFB 343 in Bielefeld.     

Jackknifing in generalized linear models

In a generalized linear model, the jackknife estimator of the asymptotic covariance matrix of the maximum likelihood estimator is shown to be consistent. The corresponding jackknife studentized statistic is asymptotically normal. In addition, these results remain true even if there exist unequal dispersion parameters in the model. On the other hand, the variance estimator and the studentized statistic based on the standard method (substitution and linearization) do not enjoy this robustness property against the presence of unequal dispersion parameters.This research was supported by an Operating Grant from the Natural Science and Engineering Research Council of Canada.     

The Berry-Esseen bound for studentized U-statistics

We establish the Berry-Esseen bound O(n/2) for a studentized U-statistic under the assumption of existence of the (2+)-th moment.     

学生化U-统计量的中心极限定理

在β（１＜β３／２）阶矩存在的条件下，我们证明了学生化Ｕ 统计量服从中心极限定理．