供应链网络可靠性的多层Bayes估计模型

对供应链网络可靠性进行界定和分析,以节点企业间的协同为基础,得到可靠度计算模型,以此为依据采集实证样本无失效运行的数据.根据供应链网络可靠性统计特性,建立一种多层Bayes估计方法,应用于样本可靠性评估中.在估计失效率的基础上,对供应链网络可靠度进行估计,对仿真结果进行分析,显示多层Bayes估计方法应用效果较好,精确度高,反映了参数不确定性对供应链网络可靠性的影响,能够较好地解决依据无失效数据判定供应链网络可靠性水平的问题.     

处理网络计划的不确定性的一种进度模型

在应用网络计划技术进行工程项目管理中,常常需要对工程项目中潜在的不确定性进行处理,而传统的网络计划技术中,缺乏对工程项目中潜在的不确定性的处理。章通过建立评估不确定性的影响因子区间的模型与方法,提出了在工程项目的进度管理中动态地对潜在的不确定性进行处理的有效方法。     

基于节点重要性的供应链网络风险跨层次评估研究

近几年,随着扰动供应链稳定运作事件的多次发生,供应链风险评估研究日益受到人们重视,然而以往研究大多针对某一企业的具体风险进行评估,鲜有从整个供应链网络角度进行系统的分析.对此,文章结合复杂网络节点位置特性的相关理论,首先建立了基于多指标最优策略的企业重要性评估模型,实现了对企业重要性的综合评估.其次,将企业重要性评估结果作为过渡参量引入供应链网络风险评估模型中,实现从"微观企业风险"到"宏观供应链网络风险"的跨层次评估.最后实证研究表明,多属性指标的企业重要性评估模型和供应链网络风险综合评估模型可以对企业综合重要性以及供应链网络综合风险进行更为准确的定量评判.     

基于多层次灰色关联分析的创新合作网络节点评估研究

基于网络中心性指标的分析,有效融合网络度中心度、网络中介中心度、网络接近中心度、网络特征向量中心度四种中心性指标的优点,构建多层次灰色关联分析的节点综合评估模型,对网络中节点的重要性进行综合评估,对网络中核心节点予以判定.结合新一代信息技术领域专利合作网络的数据进行分析,结果表明模型在节点重要性排序结果上比以往的方法更科学,不同节点重要性区分度也更高.     

基于区间层次分析法的网络安全评估研究

针对网络安全评估存在的不确定性,提出了一种新的基于区间层次分析法的网络安全评估方法.首先建立了多层次的网络安全评估指标体系,然后利用区间层次分析法确定了网络安全评估指标的区间数权重.在此基础上,结合区间数理论建立了基于区间层次分析法的网络安全评估模型.最后结合评估实例对某公司办公自动化网络进行了安全评估,验证了方法的可行性和有效性.     

基于无线传感器网络的可靠性模型及上界

无线传感器网络的技术应用与开发对网络的性能要求越来越高.从保证网络可靠性和服务质量角度考虑无线传感器网络性能参数.给出了保障服务质量的网络可靠性数学模型,由于可靠性计算是NP难问题,进一步研究该网络模型的可靠性上界.通过利用点割集代替极小割的方法提出了可靠性上界的计算公式.仿真结果表明,上界结果对评估网络可靠性精确值的近似效果较好.     

网络小额贷款业务个人信用风险评估——基于DNN-SMOTEENN-ExtraTrees组合模型

针对网络小额贷款业务,构建组合模型DNN-SMOTEENN-ExtraTrees评估网络小贷信用风险.首先利用SMOTEENN算法处理样本数据中“好”和“坏”样本分布极端不平衡情况,再利用极端随机数算法ExtraTrees对特征重要性进行评估并剔除无关变量,最后采用深度神经网络DNN评估网络小贷个人信用风险.通过召回率、精确度、F1值和AUC值等模型性能评价指标,与BP神经网络模型、Logistic回归及支持向量机比较,发现组合模型分类能力更显著,泛化能力更加优异,更适合数据规模大、维度高的网络小贷市场评估信用风险.     

基于复杂网络理论的企业营销网络鲁棒性评估

在不确定性和危机出现的情况下,鲁棒性将是企业营销网络是否能正常发挥产品营销功能的决定性因素.在对企业营销网络的的复杂性和不确定性分析的基础上,给出了企业营销网络鲁棒性定义.依据重点保护"关键节点"来提高企业营销网络鲁棒性的思路提出业营销网络节点重要度评估方法,并通过算例对评估方法进行数值分析,验证方法的有效性和适用性.     

基于韧性度的低轨卫星通信网络抗毁性度量及优化

低轨卫星通信网络的抗毁性是描述网络安全可靠的有效工具,在网络体系结构设计和路由策略等领域得到了广泛的应用。根据低轨卫星通信网络中卫星在轨道平面内移动,需要不断进行切换的特点,从建立抗毁性测度模型以及网络抗毁性优化两个角度来评估和提高网络抗毁性,提出一种基于韧性度的低轨卫星通信网络抗毁性度量方法。通过对移动模型以及切换模型的结构分析,对每种结构以一定概率出现的低轨卫星通信网络,应用韧性度函数,求得网络在某个时刻及某一段时间段内的抗毁性,并针对切换模型的不足之处进行优化,用赋权韧性度来体现优化的效果,得到了优化后的网络抗毁性。以铱星系统为应用实例进行仿真,结果表明:任意时刻网络的抗毁性跟拓扑结构的韧性度值有关,并且是一种线性关系,即随着韧性度的增加,其抗毁性也增加。通过对铱星通信系统切换模型的优化,网络的抗毁性与平均抗毁性都得到了提升,说明本文所构建模型的有效性和实用性。     

Ad Hoc网络中的区域划分和资源分配问题

建立了Ad Hoc网络中的区域划分和资源分配的模型,重点对网络的最少小区数覆盖、抗毁性、节点分簇等问题进行了研究,并对节能性、通信质量评估进行了初步探讨.     

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The log-normal distribution is a common choice for modeling positively skewed data arising from many practical applications.This article introduces a new method of constructing confidence interval for a common mean shared by several log-normal populations through confidence distributions, which combines all information from independent sources. We develop a non-trivial weighting approach by taking account of the sample variances of related quantities to enhance efficiency. Combined confidence distributions are used to construct confidence intervals for the common mean and a simplified version of one existing method is also proposed. We conduct simulation studies to evaluate the performance of the proposed methods in comparison with existing methods. Our simulation results show that the weighting approach yields shorter interval length than the non-weighting approach. The newly proposed confidence intervals perform very well in terms of empirical coverage probability and average interval length. Finally, applications of the proposed methodology is illustrated through three real data examples.     

位置刻度分布族中参数的两步置信区间

寻找一些分布中的参数的具有预先给定宽度和预先给定覆盖概率的置信区间是令人感兴趣的，对于位置刻度分布族中位置参数和刻度参数，这种类型的置信区间的存在性问题已在文献中被解决，本文利用两步抽样，具体地构造出这样的固定宽度置信区间，此外，对于Cauchy分布，第一阶段的最优抽样量和一些统计量的分位点也被计算出，所得到的结果具有应用价值。     

Bootstrap approximation of wavelet estimates in a semiparametric regression model

The inference for the parameters in a semiparametric regression model is studied by using the wavelet and the bootstrap methods. The bootstrap statistics are constructed by using Efron＇s resampling technique, and the strong uniform convergence of the bootstrap approximation is proved. Our results can be used to construct the large sample confidence intervals for the parameters of interest. A simulation study is conducted to evaluate the finite-sample performance of the bootstrap method and to compare it with the normal approximation-based method.     

广义Lorenz 曲线的非参数统计推断

本文讨论了广义Lorenz 曲线的经验似然统计推断. 在简单随机抽样、分层随机抽样和整群随机抽样下, 本文分别定义了广义Lorenz 坐标的pro le 经验似然比统计量, 得出这些经验似然比的极限分布为带系数的自由度为1 的χ² 分布. 对于整个Lorenz 曲线, 基于经验似然方法类似地得出相应的极限过程. 根据所得的经验似然理论, 本文给出了bootstrap 经验似然置信区间构造方法, 并通过数据模拟, 对新给出的广义Lorenz 坐标的bootstrap 经验似然置信区间与渐近正态置信区间以及bootstrap 置信区间等进行了对比研究. 对整个Lorenz 曲线, 基于经验似然方法对其置信域也进行了模拟研究. 最后我们将所推荐的置信区间应用到实例中.     

Bootstrapping the mode

The problem of constructing bootstrap confidence intervals for the mode of a density is considered. Estimates of the mode are derived from kernel density estimates based on fixed and data-dependent bandwidths. The asymptotic validity of bootstrap techniques to estimate the sampling distribution of the estimates is investigated. In summary, the results are negative in the sense that a straightforward application of a naive bootstrap yields invalid inferences. In particular, the bootstrap fails if resampling is done from the kernel density estimate. On the other hand, if one resamples from a smoother kernel density estimate (which is necessarily different from the one which yields the original estimate of the mode), the bootstrap is consistent. The bootstrap also fails if resampling is done from the empirical distribution, unless the choice of bandwidth is suboptimal. Similar results hold when applying bootstrap techniques to other functionals of a density.     

伽玛分布参数的最短置信区间

Γ(β,θ)分布中未知参数θ的最短置信区间实际上是一个条件极值问题,它能转化成一个方程组可用数值方法迭代求解。在假设参数β=1,置信水平为0.95的条件下,比较了常用置信区间与最短置信区间的长度,结果表明:两者长度的绝对差d(n)和相对差e(n)均随样本容量n的增大而减小,当n≤9时,e(n)≥10%。这说明在小样本下,研究参数的最短置信区间是必要的。     

Minimum volume confidence regions

In this paper a simple method of constructing a 1 ? α confidence region that has the smallest volume among all ? α confidence regions based on a pivotal quantity is presented. To illustrate the usefulness of the method, its application to a standard problem is included.     

均匀分布参数的最短置信区间

将求均匀分布未知参数的最短置信区间转化为条件极值问题,给出了均匀分布参数的最短置信区间,推广了原有的结论.     

Comparison of ‘Tail Method’ Exact Confidence Limits for the Difference of Binomial Probabilities

Suppose that Y ₁ and Y ₂ are independent and have Binomial(n ₁,p ₁) and Binomial (n ₂,p ₂) distributions respectively. Also suppose that θ=p ₁−p ₂ is the parameter of interest. We consider the problem of finding an exact confidence limit (either upper or lower) for θ. The solution to this problem is very important for statistical practice in the health and life sciences. The ‘tail method’ provides a solution to this problem. This method finds the exact confidence limit by exact inversion of a hypothesis test based on a specified test statistic. Buehler (J. Am. Stat. Assoc. 52, 482–493, 1957) described, for the first time, a finite-sample optimality property of this confidence limit. Consequently, this confidence limit is sometimes called a Buehler confidence limit. An early tail method confidence limit for θ was described by Santner and Snell (J. Am. Stat. Assoc. 75, 386–394, 1980) who used the maximum likelihood estimator of θ as the test statistic. This confidence limit is known to be very inefficient (see e.g. Cytel Software, StatXact, version 6, vol. 2, 2004). The efficiency of the confidence limit resulting from the tail method depends greatly on the test statistic on which it is based. We use the results of Kabaila (Stat. Probab. Lett. 52, 145–154, 2001) and Kabaila and Lloyd (Aust. New Zealand J. Stat. 46, 463–469, 2004, J. Stat. Plan. Inference 136, 3145–3155, 2006) to provide a detailed explanation for the dependence of this efficiency on the test statistic. We consider test statistics that are estimators, Z-statistics and approximate upper confidence limits. This explanation is used to find the situations in which the tail method exact confidence limits based on test statistics that are estimators or Z-statistics are least efficient.     

Sample size for the Z test and its confidence interval

The statistical power of a significance test is closely related to the length of the confidence interval (i.e. estimate precision). In the case of a Z test, the length of the confidence interval can be expressed as a function of the statistical power.