具有测量误差的非线性模型的Bayes估计

测量中大量的函数模型都是非线性回归模型．当回归变量含有一定的测量误差时，我们得到非线性测量误差模型．本讨论了这种模型中未知参数具有正态先验分布时的参数Bayes估计方法，并对这种估计进行了影响分析，证明了删除模型与均值漂移模型中参数的Bayes估计相同，利用Cook统计量给出了删除模型下参数的Bayes估计的影响度量．     

判定数据对边界条件下线性模型影响的二个度量

研究数据对模型的影响是一个重要问题．主要考察数据对边界条件下线性回归模型的影响．从度量精度方面建立了二个影响度量,并讨论了与相关系数之间的关系．     

混合线性模型效应参数的影响分析

该文考虑非平衡混合线性模型的影响分析及强影响点的探测问题．文中导出了在单点剔除模型下效应参数的经验Ｂａｙｅｓ估计与原模型下对应估计的相互关系．利用Ｃｏｏｋ距离和协方差比统计量，我们分别给出了剔除单个数据点对固定效应和随机效应参数估计以及估计精度的影响度量．最后，给出具体的计算方法，并对一实例进行分析．     

拓扑群作用下度量空间中链回归点集的研究

仿造度量空间中链回归点的定义,给出了拓扑群作用下度量空间中G-链回归点的概念,并将度量空间中链回归点的一些结论,推广到拓扑群作用下度量空间中,得到如下结果:1)同胚伪等价映射f的G-链回点集等于它的逆映射f~(-1)的G-链回归点集;2)伪等价映射f的G-链回点集和G-链等价集对G强不变;3)同胚等价映射f的G-链回点集f对强不变.4)等价映射f限制在它的G-链回归点集上形成的G-链回归点集就是等价映射f在度量G-空间X上形成的G-链回归点集.这些结果丰富了拓扑群作用下度量空间中G-链回归点的理论.     

权回归与一般影响度量

权回归模型是近年来文献中较多涉及的一种.本文从回归诊断的角度进一步研究了它的若干问题.如扰动、影响度量及估计效率等.由于这类模型的一般性,因而本文的结果均可看作回归诊断中较成熟的一些结论的推广.     

如何发现世界上最安静的敌潜艇

我们建立了一个数学模型；用以探测一个大型移动目标，譬如说一艘潜艇．并且计算出潜艇的大小和它移动的速度、方向．探测过程仅依赖于一个普通噪声环境受到的扰动我们作了一系列合理的假设，分析了潜艇的运动以及它对背景噪声场的影响．运动潜艇吸收、反射噪声声波，由此产生的多普勒效应，是原噪声场的一个扰动．这种扰动可被探测器（如压电麦克风）捕获．在对潜艇的形状与材料作了假设的基础上，我们推导出在一个单频单幅噪声场中潜艇回波的频谱分布．这个频谱分布类似伞形，简称伞形分布．频谱带宽可用于计算潜艇航速．伞形分布曲线下的面积可用于计算潜艇等效截面积，根据频谱的中心频率与噪声频率的差异，可计算出它的移动方向我们引入了对数频率坐标．基于卷积理论，计算出了在宽带噪声场中潜艇的回波．这样，通过反卷积，就可以简化为上述己解决的若这艘潜艇置于一单频单幅噪声场中应有的情况．根据反卷积后的回波分布，即可根据本文中提到的方法，得到需要的结果．我们还把这个模型推广到实际中，给出了一个伺服系统和一个可行的方案．文中最后就模型优缺点和问题本身的局限性作了讨论．考虑到实际的应用，提出了一些新的构想，并预想了这类问题今后可能的发展方向．     

点可迁图的限制边连通度

设Ｓ是连通图Ｇ的边子集．如果Ｇ－Ｓ不连通而且不含孤立点，那么称Ｓ是Ｇ的一个限制边割，Ｇ中所有限制边割中最小边数称为Ｇ的限制边连通度，记为λ＇（Ｇ）．限制边连通度是对传统边连通度的推广，而且是计算机互连网络容错性的一个重要度量．点可迁图是一类重要的网络模型．本文证明了如下结论： 设 Ｇ是连通的点可迁图．如果 Ｇ的点数ｎ≥ ４，而且点度ｋ≥ ２，那么或者λ＇（Ｇ）＝ ２ｋ－２，或者ｎ是偶数，Ｇ含三角形且存在整数ｍ≥２，使得ｋ≥λ＇（Ｇ）＝ｎ／ｍ≤２ｋ－３．关     

线性回归诊断的若干问题

本文对于线性回归诊断提出了几种新的模型和方法。我们首次研究了方差加权和均值漂移的混合模型,得到了相应的诊断统计量。本文还引入了罚函数方法,并以此为工具,讨论了若干有偏估计的影响度量,最后,本文提出了基于重心的诊断统计量,对于识别异常点有较好的效果。     

两个多面体之交上的最佳逼近与保凸回归问题

本文提出了一种求Hilbert空间中给定点x0在两个多面体K’与K”之交上的最佳逼近的算法，它把问题化归为有限次求点在K’与K”中的最佳逼近的问题．由于保凸回归问题可表述为求某点x0在两个锐锥之交上的最佳逼近问题，故结合熟知的锐锥逼近的PAVA算法即可得到保凸回归的有限算法．文章还计算了一个保凸回归问题的实例．     

强链回归集与强跟踪性

为了研究强跟踪性，本文给出了强链回归集的定义．证明了：若度量空间上的一个连续自映射有强跟踪性，则其强链回归集与极限集相同．     

运用ARIMA模型检验BPR对销售业绩影响

本文探讨BPR(业务流程再造 )对企业经营业绩影响的评价方法。作者采用ARIMA模型 ,针对某公司实施结果 ,以销售额为评价标准 ,将时间序列分为BPR实施前后两组进行分析 ,发现BPR具有明显提高销售额功效 ,提高幅度为 84万。文章介绍的方法可以推广到任一管理措施的效果评价     

基于修正区间模糊Shapley值的“一带一路”PPP项目利益分配模型

多方合作的“一带一路”基础设施PPP项目是互联互通的基石,公平合理地分配项目利益则是保障其顺利实施的关键。为了构建科学的利益分配模型,考虑项目参与方组建的合作联盟预期收益为模糊数且分配时受多种因素影响,本文首先,基于区间模糊Shapley值构建“一带一路”PPP项目利益分配初始模型;然后,识别出利益分配影响因素并量化参与方的投入分担程度,据此修正初始的利益分配模型;最后,结合实际案例,将该模型与现存方法进行对比分析,结果显示本模型的可操作性更强,针对影响因素系数的测算更为精准,整体具有显著优势。     

A generalized Borgonovo's importance measure for fuzzy input uncertainty

In this work, the way in which fuzzy uncertainty in a model's output is apportioned to fuzzy uncertainty in model inputs is studied through a sensitivity analysis. Here, an optimization technique is employed to obtain the membership functions of the fuzzy structural response, for which a global sensitivity indicator is introduced to measure the influence of fuzzy input uncertainty on fuzzy output uncertainty. The global sensitivity indicator is the important measure of the fuzzy input uncertainty, which extends Borgonovo's measure. In this study, the mathematical properties of the important measure of the fuzzy input uncertainty are discussed and proved. The results of numerical examples and engineering examples show that the proposed importance measure can effectively describe the effect of fuzzy input uncertainty on fuzzy structural response. When the sensitivity indicator is larger, the basic fuzzy-valued variable becomes more important. The sensitivity indicators of the fuzzy structural response can give an essential importance sequence of all the basic fuzzy-valued variables and identify key contributing fuzzy-valued variables. The sensitivity indicators can provide the availability guidance to reduce the number of basic variables and optimize the fuzzy response model.     

Dynamic risk measures under model uncertainty

In this paper we consider the notion of dynamic risk measures, which we will motivate as a reasonable tool in risk management. It is possible to reformulate an example of such a risk measure in terms of the value functions of a Markov decision model (MDM). Based on this observation the model is generalized to a setting with incomplete information about the risk distribution which can be seen as model uncertainty. This issue can be incorporated in the dynamic risk measure by extending the MDM to a Bayesian decision model. Moreover, it is possible to discuss the effect of model uncertainty on the risk measure in binomial models. All investigations are illustrated by a simple but useful coin tossing game proposed by Artzner and by the classic Cox–Ross–Rubinstein model.     

Applying relation algebra and RelView to measures in a social network

We present an application of relation algebra to measure agents’ ‘strength’ in a social network with influence between agents. In particular, we deal with power, success, and influence of an agent as measured by the generalized Hoede–Bakker index and its modifications, and by the influence indices. We also apply relation algebra to determine followers of a coalition and the kernel of an influence function. This leads to specifications, which can be executed with the help of the BDD-based tool RelView after a simple translation into the tool’s programming language. As an example we consider the present Dutch Parliament.     

Influence Analysis for Linear Measurement Error Models

In this paper, we present a unified diagnostic method for linear measurement error models based upon the corrected likelihood of Nakamura (1990, Biometrika, 77, 127–137). Both global influence and local influence are discussed. The case-deletion model and mean-shift outlier model are considered, and they are shown to be approximately equivalent. Several diagnostic measures are derived and discussed. It is found that they can be written in terms of the residual and leverage measure. Some existing results are improved. Numerical example illustrates that our method is useful for diagnosing influential observations.     

Influence measure based on probabilistic behavior of regression estimators

An influence measure for investigating the influence of deleting an observation in linear regression is proposed based on geometric thoughts of the sampling distribution of the distance between two estimators of regression coefficients computed with and without a single specific observation. The covariance matrix of the above sampling distribution plays a key role in deriving the influence measure. It turns out that geometrically, this distance is distributed entirely along the axis associated with the nonnull eigenvalue of the covariance matrix. The deviation of the regression coefficients computed without an observation from the regression coefficients computed with the full data is reflected in the eigenvalue of the covariance matrix which can be used for investigating the influence. The distance is normalized using the associated covariance matrix and this normalized distance turns out to be the square of internally studentized residuals. Illustrative examples for showing the effectiveness of the influence measure proposed here are given. In judging the influence of observations on the least squares estimates of regression coefficients, Cook’s distance does not work well for one example and therefore we should be cautious about a blind use of the Cook’s distance.     

苏州外资制造业经济增长因素贡献率测算模型与分析

在经济增长因素分析中,人们常用生产函数来分析经济增长过程,测算各要素对经济增长的贡献率.本文利用柯布-道格拉斯生产函数给出苏州外资制造业经济增长的六个影响因素贡献率测算模型与分析,由于六个影响因素之间存在多重共线性,为消除多重共线性,使模型合理,本文使用主成分回归建立模型,结果令人满意。     

creditrisk~+模型在信贷组合信用风险度量中的应用研究

creditrisk~+模型以泊松分布为理论基础,是一种输入数据较少、计算复杂度较小、便于在银行实际中应用和推广的信贷组合信用风险度量模型.本文在分析国外creditrisk~+模型频带划分缺陷基础上,改用加权平均的频带划分方法,并提出了新的违约率参数的确定办法,利用creditrisk~+模型对大连市商业银行某支行224笔中小公司贷款的非预期损失进行了实证计算.结果发现:creditrisk~+模型可以有效地计量信贷组合的非预期损失且可提高我国商业银行经济资本管理效率.