Long‐range dependence and performance in telecom networks

Telecommunications systems have recently undergone significant innovations. These call for suitable statistical models that can properly describe the behaviour of the input traffic in a network. Here we use fractional Brownian motion (FBM) to model cumulative traffic network, thus taking into account the possible presence of long‐range dependence in the data. A Bayesian approach is devised in such a way that we are able to: (a) estimate the Hurst parameter H of the FBM; (b) estimate the overflow probability which is a parameter measuring the quality of service of a network: (c) develop a test for comparing the null hypothesis of long‐range dependence in the data versus the alternative of short‐range dependence. In order to achieve these inferential results, we elaborate an MCMC sampling scheme whose output enables us to obtain an approximation of the quantities of interest. An application to three real datasets, corresponding to three different levels of traffic, is finally considered. Copyright © 2004 John Wiley & Sons, Ltd.     

A branch and bound algorithm for the robust shortest path problem with interval data

Many real problems can be modelled as robust shortest path problems on interval digraphs, where intervals represent uncertainty about real costs and a robust path is not too far from the shortest path for each possible configuration of the arc costs.A branch and bound algorithm for this problem is presented.     

两个DEA的逆问题的解法的探讨

文[1]提出了两个DEA的逆问题，并用搜索法来解．而本文根据所证的定理，对每个问题一般只要解二、三个线性规划问题就能得到答案．     

组合DEA方法与成熟度模型对项目效益的评价

为全面考虑资金、管理决策能力等因素对项目效益的影响，本运用数据包络分析与项目成熟度模型结合的方法来对企业各个项目之间的相对效益进行评价，应用结果表明该评价方法对于企业资源的最优配置、提高总体效益是十分有用的。     

非线性纵向数据模型中自相关性和随机效应的存在性检验

刻画纵向数据协方差结构有三种可能因素 ,即序列相关 (特别是一阶自相关 )、随机效应和常规的随机误差 (Diggleetal,2 0 0 2 ) .本文研究非线性纵向数据模型的自相关性和随机效应存在性的单个和联合检验 ,得到了检验的score统计量 ,并利用血浆药物渗透数据 (Davidian&Gilinan ,1 995)说明检验方法的应用 .     

Linear low-rank approximation and nonlinear dimensionality reduction

We present our recent work on both linear and nonlinear data reduction methods and algorithms: for the linear case we discuss results on structure analysis of SVD of column-partitioned matrices and sparse low-rank approximation; for the nonlinear case we investigate methods for nonlinear dimensionality reduction and manifold learning. The problems we address have attracted great deal of interest in data mining and machine learning.     

数据同化中的伴随方法的有关问题的研究

关于伴随方法应用中只能利用模式的伴随的观点,被认为是有疑问的．所作的数值模拟实验表明,对于潮波模型而言,方程的伴随能够得到与模式的伴随同样的结果:调和常数的实测值与模拟值的振幅差的绝对值的平均小于5.0 cm,迟角差的绝对值的平均小于5.0°．这些结果都能够体现渤、黄海M2分潮的基本特征．作为对比,也利用前人的方法对渤、黄海的M2分潮潮波进行了数值模拟,首先借助于历史资料和观测资料得到开边界的初始猜测,然后对开边界的初始猜测值进行调整,以得到与高度计资料之差尽可能小的模拟结果．但由于开边界的值共有72个,究竟有哪些值需要调整,需要如何调整,只有经过不断的调试,才能部分地解决这些问题．工作量大且很难得到令人满意的结果．该文实现了确定开边界条件的自动化过程,这与前人的方法相比,有无可比拟的优势．需要特别强调的是如果利用方程的伴随,可以避免繁琐而冗长的数学推导．因而说明方程的伴随也应该引起足够的重视．     

Semilinear parabolic equations involving measures and low regularity data

A detailed study of abstract semilinear evolution equations of the form is undertaken, where generates an analytic semigroup and is a Banach space valued measure depending on the solution. Then it is shown that the general theorems apply to a variety of semilinear parabolic boundary value problems involving measures in the interior and on the boundary of the domain. These results extend far beyond the known results in this field. A particularly new feature is the fact that the measures may depend nonlinearly and possibly nonlocally on the solution.

     

The impact of special days in call arrivals forecasting: A neural network approach to modelling special days

A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established, statistical methods, we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter, we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe’s leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods.