A bayesian estimator for the dependence function of a bivariate extreme‐value distribution

Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme‐value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set.     

一种LOG-MAP算法的改进迭代实现及其结构

对LOG-MAP算法进行了可并发性分析,利用可以完整接收一个N长符号传输帧的条件以及正向迭代和反向迭代中的固有对称性质,提出了LOG-MAP算法的一种修正迭代实现算法,其正向和反向迭代计算次数只需码长N的一半,故比标准LOG-MAP算法提高了一倍的处理速度,且没有空间开销的增加、同时,根据修正的迭代实现算法给出了相应的适于FPGA实现的双总线硬件结构的实现方案。     

以太网光桥的研究

根据有关网络技术和具体的实验,提出一种以太网光桥的实现方案,并对光桥的几个性能参数进行了分析讨论。     

Domains of convergence for the EM algorithm: a cautionary tale in a location estimation problem

The EM algorithm is a popular method for maximizing a likelihood in the presence of incomplete data. When the likelihood has multiple local maxima, the parameter space can be partitioned into domains of convergence, one for each local maximum. In this paper we investigate these domains for the location family generated by the t-distribution. We show that, perhaps somewhat surprisingly, these domains need not be connected sets. As an extreme case we give an example of a domain which consists of an infinite union of disjoint open intervals. Thus the convergence behaviour of the EM algorithm can be quite sensitive to the starting point.     

Bayesian analysis of outlier problems using divergence measures

A Bayesian approach is presented for detecting influential observations using general divergence measures on the posterior distributions. A sampling-based approach using a Gibbs or Metropolis-within-Gibbs method is used to compute the posterior divergence measures. Four specific measures are proposed, which convey the effects of a single observation or covariate on the posterior. The technique is applied to a generalized linear model with binary response data, an overdispersed model and a nonlinear model. An asymptotic approximation using Laplace method to obtain the posterior divergence is also briefly discussed.     

改进的OBS长度自适应门限组装算法

现有OBS长度自适应组装算法的突发数据长度门限的选择虽然随着业务流的变化而变化,克服了固定长度门限算法输出的突发数据的突发性,但仍然没有考虑业务流的自相似特性,因此不能有效降低输出突发数据的自相似程度和分组阻塞率。针对这一问题,该文提出了一种改进的OBS长度自适应门限组装算法,能根据到达的网络业务流的自相似特性自适应地调整长度门限值。仿真结果表明:在相同的调度算法下,更能降低业务流的自相似性,分组阻塞率降低一个数量级。     

货物运输车辆的调度算法

本文以上海煤炭公司为例,讨论了运煤车辆的计算机调度问题,提出一个数学模型及有效的近似算法。在算法实现中,采用了一些较为先进的数据结构,使运算速度及存储需求均有较大的改善。对实际算例计算结果表明,采用本算法进行运输车辆调度,经济效益比人工调度有明显的提高。     

Principal curves revisited

A principal curve (Hastie and Stuetzle, 1989) is a smooth curve passing through the middle of a distribution or data cloud, and is a generalization of linear principal components. We give an alternative definition of a principal curve, based on a mixture model. Estimation is carried out through an EM algorithm. Some comparisons are made to the Hastie-Stuetzle definition.     

基于Simpson公式改进的FAGM(1,1)模型及其应用研究

本文分析了传统FAGM（1,1）模型建模过程中存在的误差,提出了一种基于Simpson公式改进的FAGM（1,1）模型。首先,基于分数阶累加生成算子和分数阶累减生成算子建立分数阶FAGM（1,1）模型。其次,利用Simpson积分公式对FAGM（1,1）模型的背景值进行改进,建立SFAGM（1,1）模型。进一步,应用遗传算法确定SFAGM（1,1）模型的最优阶数以提高模型的预测精度。最后,以中国人均GDP为例,对比分析GM（1,1）模型、Simpson改进的GM（1,1）模型（SGM（1,1））、FAGM（1,1）模型、SFAGM（1,1）模型的模拟结果,并对"十三五"时期的人均GDP进行预测,其结果表明SFAGM（1,1）模型比GM（1,1）、SGM（1,1）、FAGM（1,1）在人均GDP的预测方面有更高的精度,"十三五"期间人均GDP年平均增长率为10.64%,到2020年达到83146.97元,是2010年人均GDP的2.69倍,以2010年的人均GDP为基准,到2020年将能够实现翻一番的目标。     

考虑多种安全库存策略的选址-库存问题研究

考虑多种安全设置策略的物流网络的选址-库存问题,不仅是选址、订货、运输和库存的集成优化,还需要考虑多种不同的安全库存设置和转运策略。因此,本文深入讨论了二级物流网络中的六种安全库存设置策略,构建了六种考虑不同安全库存设置的选址-库存模型。在考虑集中设置安全库存时,集中安全库存需要通过转载运输实现,因此需要将转载运输成本引入选址-库存模型之中,使新的选址-库存模型更加科学合理。另外,针对六种新的选址-库存模型,提出了基于个体成本差异分配的遗传算法,迭代搜索选址、分配、库存设置策略的优化组合。最后,通过数据实验验证了模型的有效性：（1）安全库存与转载运输之间存在此消彼长的背反关系;（2）安全库存设置和转载运输策略对总成本的影响取决于两种费率权重情况。本文的研究可以为二级物流网络的选址、订货和安全库存策略集成优化决策提供参考依据。