两个半相依模型回归系数的改进估计

对于两个半相依回归系统的未知回归系数,本文首先借鉴文献中给出的两步协方差改进估计的方法给出两种两步协方差改进估计序列,并给出其与两步估计等价的条件和均方误差意义下的优良性; 其次,我们对文献中给出的一种两步估计作简单改进,使得改进后的估计在更大的参数空间内优于最小二乘估计. 再次,本文另辟蹊径, 构造了一种新的估计,同样地,此估计也具有更好的小样本性质.本文最后一节讨论了Pitman准则下两步估计的优良性.     

线性回归系统回归系数的一种新估计

对由m个相依线性回归方程组成的线性回归系统,本文把模型蕴含的信息分为样本信息和附加信息,提出了利用逐次迭加附加信息导出未知参数估计的一种新方法。具体地,解决了如下三个问题:(1)给出了概括附加信息的统计量;(2)找到了把附加信息迭加到样本信息的方法;(3)证明了用这种方法所导出的新估计的一些重要统计性质。     

相依回归系统参数的预检验估计

对由m个相依线性回归方程组成的线性回归系统,本文提出了基于最小二乘估计和协方差改进估计的一种新型估计,即预检验估计。文章讨论了度量附加信息和样本信息之间相关程度的统计量,给出了估计的一些优良性结果,并与最小二乘估计及协方差改进估计作了比较,最后通过随机模拟验证了预检验估计所具有的良好性质。     

自适应有限元和后验误差估计——渐近准确估计

在[7]中,作者讨论了有限元误差的1-模等价估计.本文是[7]的继续,给出一种自适应有限元计算中误差的1-模渐近准确估计,即对于误差的1-模||e||_1,Ω给出可计算的估计量?,当||e||_1,Ω→0时,成立?/||e||_1,Ω→1. 本文将沿用[7]中的定义及符号.     

状态概率的E-Bayes估计与多层Bayes估计

在文献[1]中提出了参数估计的一种方法--E-Bayes估计并给出了状态概率的E-Bayes估计的定义、E-Bayes估计公式、预测模型及其在证券投资中应用,本文在此基础上将给出状态概率的多层Bayes估计、状态概率的E-Bayes估计的性质--E-Bayes估计,多层Bayes估计的关系.最后,给出模拟算例.     

关于两个猜想的证明

文献[1]提出了参数估计的一种新方法—E-Bayes估计法.对Pascal分布,给出了分布参数的E-Bayes估计的定义、E-Bayes估计公式,并提出了两个猜想,但没有给出证明.本文给出了这两个猜想的证明.     

方差分量的二次不变估计的非负改进

研究一类方差分量模型中的方差分量的估计改进问题,首先在含两个方差分量模型中给出σ21二次型估计类,并且此估计类还具有无偏性和不变性.考虑二次损失(δ-θ)2,在此估计类基础上放弃无偏性进行非负改进,不仅得到优于二次不变无偏估计类的σ21的非负二次不变估计类,而且还说明了它优于方差分析估计和最小均方误差估计,文献[5]中给出s>2时的非负改进,但是非负改进存在是有条件的,本文克服了这个缺陷.最后给出了非负改进存在的充分必要条件.     

回归系数的主相关估计及其优良性

本文在回归系数的主成分估计的基础上，提出一种新的降维估计－主相关估计，讨论了它的优良性，并用实例说明主相关估计对主成分估计的改进效果。     

关于两个猜想的证明

文献[1]提出了参数估计的一种新方法—E-Bayes估计法.对Pascal分布,给出了分布参数的E-Bayes估计的定义、E-Bayes估计公式,并提出了两个猜想,但没有给出证明.本文给出了这两个猜想的证明.     

一类乘积形式的离散不等式及其应用

该文在文献[2]的基础上,研究了一类新的乘积形式的离散不等式.把参考文献中不等式右端第一个因子中包含的未知函数u推广成未知函数的幂函数u~2,运用变量替换技巧、放大技巧、微分中值定理、反函数技巧、常量与变量的辩证关系,给出了不等式中未知函数的估计.最后,阐述了所得的结果可以用来给出乘积形式差分方程解的绝对值的上界估计.     

Empirical likelihood analysis of the Buckley-James estimator

The censored linear regression model, also referred to as the accelerated failure time (AFT) model when the logarithm of the survival time is used as the response variable, is widely seen as an alternative to the popular Cox model when the assumption of proportional hazards is questionable. Buckley and James [Linear regression with censored data, Biometrika 66 (1979) 429-436] extended the least squares estimator to the semiparametric censored linear regression model in which the error distribution is completely unspecified. The Buckley-James estimator performs well in many simulation studies and examples. The direct interpretation of the AFT model is also more attractive than the Cox model, as Cox has pointed out, in practical situations. However, the application of the Buckley-James estimation was limited in practice mainly due to its illusive variance. In this paper, we use the empirical likelihood method to derive a new test and confidence interval based on the Buckley-James estimator of the regression coefficient. A standard chi-square distribution is used to calculate the P-value and the confidence interval. The proposed empirical likelihood method does not involve variance estimation. It also shows much better small sample performance than some existing methods in our simulation studies.     

Improved ARMA spectral estimation

The autoregressive (AR) spectral estimator has been studied by several authors, Parzen [10], Burg [3], and Marple [7] to name but a few. Even though the results of Burg and later results of Nuttal [9], Ulrych and Clayton [14] and also Marple [7] significantly improved the AR spectral estimator, it still is somewhat disappointing for narrow band signals or for nearly noninvertible auroregressive moving average (ARMA) data. To circumvent the difficulties, while at the same time introducing a more robust estimator, several authors have suggested the use of the ARMA spectral estimator (e.g. Morton and Gray [8] and Cadzow [4]). In this paper, a new ARMA spectral estimator is introduced which, using a recent result of Tiao and Tsay [12], makes use of dynamic prefiltering. It seems to perform better than previously defined ARMA spectral estimators and the AR spectral estimators of Burg or Marple. Examples are given which include data which is ARMA and data which is not ARMA. Several references to work in this area are included.     

一个关于凸集强分离的充分必要条件及其应用

该文首先给出了关于两个非紧凸集可被强分离的充分必要条件，它是[1，2]中一个定理的改进，然后利用它改进了[4-7]中一些定理和[8]中的一个结果．     

对称连续分布函数的最优不变估计

该文考虑了未知对称连续分布函数的不变估计问题.连续分布函数在单调变换群下是不变的^[1], 但这个变换群不能保证对称分布函数的不变性.于是, 所要研究的判决问题在单调变换群下不再是不变的. 为了保证判决问题不变性, 考虑一个新的变换群—单调奇变换群, 它确保了所研究的判决问题的不变性.注意到对称分布函数零点的特殊性质, 即, 对任一对称分布函数F, 均有F(0)=1/2,通过视零点为一伪观察值, 得到了所有的非随机化不变估计, 并在不变估计中找到了最优不变估计.     

强混合误差回归函数小波估计的Berry-Esseen界

设回归模型 Y_ni=g(t_ni)+ε_ni, i =1,…, n, 其中{t_ni} 为固定设计点列, g(?) 是定义在[0,1]上的未知函数, {ε_ni}为随机误差. 该文主要讨论了误差为强混合序列情形下, 回归函数g(?)小波估计的Berry-Esseen 界, 其界可达 O(n-^1/6).     

Combining empirical likelihood and generalized method of moments estimators: Asymptotics and higher order bias

This paper proposes an estimator combining empirical likelihood (EL) and the generalized method of moments (GMM) by allowing the sample average moment vector to deviate from zero and the sample weights to deviate from n⁻¹. The new estimator may be adjusted through free parameter δ∈(0,1) with GMM behavior attained as δ?0 and EL as δ?1. When the sample size is small and the number of moment conditions is large, the parameter space under which the EL estimator is defined may be restricted at or near the population parameter value. The support of the parameter space for the new estimator may be adjusted through δ. The new estimator performs well in Monte Carlo simulations.     

On time integration error estimation and adaptive time stepping in structural dynamics

In this paper we present two error estimators resp. indicators for the time integration in structural dynamics. Based on the equivalence between the standard Newmark scheme and a Galerkin formulation in time [1] for linear problems a global time integration error estimator based on duality [3] can also be derived for the Newmark scheme. This error estimator is compared to an error indicator based on a finite difference approach in time [2]. Finally an adaptive time stepping scheme using the global estimator and the local indicator is presented. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A note on a consistent estimator of a mixing distribution function

Summary In this note, we will study a consistent estimator of a mixing distribution function (mixing d.f.). The estimator discussed in this note is that of Choi and Bulgren [4]. Since there is some doubt about the way of proving Lemma in [4] which is used for showing the consistency of the estimator in [2], [3] and [4], we will give different lemmas. We will show that their result (which is still true by using our lemmas) holds under a weaker assumption than theirs. The existence of the estimator is not discussed in [4]. So, we will give conditions under which the existence is guaranteed. The research was supported in part by Scientific Research Fund from the Ministry of Education of Japan. No. 564076.     

An H‐matrix Type Preconditioner For Frictional Contact Problems

The bilateral or unilateral contact problem with Coulomb friction between two elastic bodies is considered [1]. An algorithm is introduced to solve the resulting finite element system by a non‐overlapping domain decomposition method [2, 3]. The global problem is transformed to a independant local problems posed in each bodie and a problem posed on the contact surface (the interface problem). The solution is obtained by using a successive approximation method, in each step of this algorithm we solve two intermediate problems the first with prescribed tangential pressure and the second with prescribed normal pressure [8]. Our preconditioner construction is based on the application of the H‐matrix technique [6, 7] together with the representation of the H^1/2 seminorm by a sum of partial seminorms [4]. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)