风险相关性下的信用风险、市场风险和操作风险集成度量

商业银行各种风险之间相关性的存在,对其整体风险的度量产生重要影响。本文针对商业银行的信用风险、市场风险和操作风险这三类主要风险,在考虑相关性基础上给出了风险集成过程,通过copula函数和蒙特卡洛模拟方法计算了商业银行的整体风险,同时研究了风险分散化效应和在不同copula函数下整体风险的变化情况。最后以主流文献中的数据做了实证分析,结果显示本文提出方法能够很好的描述风险损失之间的相关性,同时在能够抵御相同风险的情况下考虑相关性下的在险值与简单相加得到的在险值相比要小,这能为银行业提高资金利用率提供了一定的理论和方法依据。     

采用单测量源的拓扑推断算法

为了减少拓扑推断中需要时钟同步和节点间合作的限制,提出了一种仅需要单个测量源并且不需要时钟同步的运输车测量方法,该方法可以测量目的地址共享链路的排队时延。设计了根据排队时延推断拓扑结构的算法。理论证明了基于运输车测量方法和排队时延推断网络拓扑的可行性和正确性,并通过NS2进行了仿真,仿真结果表明基于运输车测量方法和排队时延能够准确的推断网络拓扑结构。     

批判性思维倾向与英语学习的相关性研究

为探究批判性思维倾向与英语学习的关系,文章采用了香港理工大学彭美慈等修订的CTDI-CV问卷调查表,对大连理工大学机械学院机械制造及其自动化专业的普通班及英语强化班的06、07、08级全日制在校本科生进行了调查。结果表明,批判性思维倾向与英语教学强度、英语学习成绩和平时学习英语的时间存在一定的相关性。     

Baseball players with the initial “K” do not strike out more often

It has been claimed that baseball players whose first or last name begins with the letter K have a tendency to strike out more than players whose initials do not contain the letter K. This “result” was achieved by a naive application of statistical methods. We show that this result is a spurious statistical artifact that can be reversed by the use of only slightly less naive statistical methods. We also show that other letters have larger and/or more significant effects than the letter K. Finally, we show that the original study applied the wrong statistical test and tested the hypothesis incorrectly. When these errors are corrected, most of the letters of the alphabet have a statistically significant strikeout effect.     

面向开放式团队知识创新的动态标签关联网研究

知识创新是企业提高核心竞争力最重要的手段之一。当今世界经济一体化,全球创新方向正在向开放式创新转变。开放式创新环境下,跨组织、跨地域的开放式创新团队成为知识创新的主要载体。社会化标签能够使团队成员按照自己对知识的认知方式进行知识交互与协作,有利于促进开放式团队知识创新。但是由于标签标注的自由性,如何建立标签之间的关联关系以更有效地支持开放式环境中基于标签的知识管理与知识创新成为该领域的关键议题。本文研究并提出了面向开放式团队知识创新的动态标签关联网方法,并对该方法进行了应用研究,通过比较实验组和对照组的应用结果,可以发现面向开放式团队知识创新的动态标签关联网方法能够动态地展示标签之间的关联关系,从而支持开放式环境中的团队知识创新。     

基于相关系数的钢材需求量组合预测

近年来我国钢材消费量和产量大幅提升,在1997～2006年的10年里分别增长了308%和367%,虽然同期内国内铁矿石产量也增加了119%,但仍无法满足钢铁工业的需求,对外依存度从28.5%提升到51.8%。在对钢材以及铁矿石消费量的预测实践中,由于不同的预测方法能够提供不同的有用信息,其预测精度往往也存在差异,为了分散预测的风险,文章采用基于相关系数的组合预测方法对我国未来的成品钢材需求量进行预测,并对预测结果进行了深入分析。结果表明,基于相关系数的组合预测模型在预测准确性和适应性方面均能得到一定改善,能很好地对钢材需求量进行科学、有效的预测。     

Kernel partial correlation: a novel approach to capturing conditional independence in graphical models for noisy data

Graphical models capture the conditional independence structure among random variables via existence of edges among vertices. One way of inferring a graph is to identify zero partial correlation coefficients, which is an effective way of finding conditional independence under a multivariate Gaussian setting. For more general settings, we propose kernel partial correlation which extends partial correlation with a combination of two kernel methods. First, a nonparametric function estimation is employed to remove effects from other variables, and then the dependence between remaining random components is assessed through a nonparametric association measure. The proposed approach is not only flexible but also robust under high levels of noise owing to the robustness of the nonparametric approaches.     

Correlation coefficient of response variances in compound noise and product array experiments

In robust parameter design, a compound noise experiment has been frequently used for reducing the number of experimental runs compared to a product array experiment. However, the results obtained by the compound noise experiment and the product array experiment are often much different. This paper derives an expression of the correlation coefficient of response variances in the compound noise and the product array experiments, which gives an explanation of that difference.     

基于多尺度分析的小麦价格预测研究

本文基于分解-重构-集成的思想,构建了一个多尺度组合预测模型,选取小麦作为粮食的代表,预测其价格走势。首先,运用集合经验模态分解方法(EEMD)分解价格序列,然后,用灰色关联分析方法对分量序列进行重构,重构为高频、中频、低频和趋势项四个部分,并从不规则因素、季节因素、重大事件和世界经济水平等方面对这四个部分波动特点进行解释,针对不同特点的分量选择不同的方法进行预测,最后对各预测结果用支持向量机集成,并与其他预测模型的预测结果进行比较。实证结果表明,本文构建的多尺度组合模型的预测效果优于灰色预测GM(1,1)、BP神经网络、SVM方法、ARIMA模型等单模型方法和ARIMA-SVM组合模型以及基于EMD和EEMD分解的其他多尺度组合模型。     

Efficient regression modeling for correlated and overdispersed count data

Abstract

The objective of this paper is to propose an efficient estimation procedure in a marginal mean regression model for longitudinal count data and to develop a hypothesis test for detecting the presence of overdispersion. We extend the matrix expansion idea of quadratic inference functions to the negative binomial regression framework that entails accommodating both the within-subject correlation and overdispersion issue. Theoretical and numerical results show that the proposed procedure yields a more efficient estimator asymptotically than the one ignoring either the within-subject correlation or overdispersion. When the overdispersion is absent in data, the proposed method might hinder the estimation efficiency in practice, yet the Poisson regression based regression model is fitted to the data sufficiently well. Therefore, we construct the hypothesis test that recommends an appropriate model for the analysis of the correlated count data. Extensive simulation studies indicate that the proposed test can identify the effective model consistently. The proposed procedure is also applied to a transportation safety study and recommends the proposed negative binomial regression model.