基于改进信息熵的抢修操作过程复杂性测度模型

抢修操作过程复杂性既是抢修人员准确衡量抢修操作难易程度及不确定性的重要指标,又是辅助指挥员进行抢修任务分配及抢修资源调度的重要参考,还是进行抢修时间估计的重要依据。然而,目前没有合适的抢修操作过程复杂性测度方法。考虑到普通信息熵极大值公理与抢修操作过程复杂性的特征不相符,利用改进的信息熵建立抢修操作过程的复杂性测度模型,并利用回归分析法通过实例验证,模型测度结果可以客观地反映抢修操作过程的复杂性。     

一类半参数可靠性回归模型估计的强相合性

对半参数回归模型Yi=xiβ+g(ti)+εi,i=1,2,…,n定义了参数β与回归函数g(·)的估计,并证明了 它们的强相合性。     

机械系统维修性预计方法分析与应用

通过对美国及前苏联维修性预计方法的分析,确定前苏联“以类似产品的维修性多元线性回归方程作为新产品维修性预计模型”的方法为机械系统维修性预计方法.在限定的条件下,采集了装甲车辆机械系统的维修性数据,并应用这些数据进行分析得出相应的机械系统维修性预计模型.     

Laplace分布的均值被半序约束时的极大似然估计

讨论了Laplaoe分布中,方差已知的情况下,均值θ在简单半序约束下的最小L_1保序回归(ML_1IR),就最小L_1保序回归的唯一性以及一些其它性质予以了讨论,并且给出了计算方法。     

线性回归模型系数岭估计的改进研究

对作者提出的c-k型估计,进行了进一步的研究.证明利用Stein式压缩技术可以改进岭估计(在均方误差意义下);同时给出了参数的最优值满足的条件.证明了c-k型估计的可容许性.文中的方法为病态线性回归模型系数的有偏估计提供了改进的技术途径.     

Small world,big guns: globalization,interstate security networks and conventional weapons imports

With the burgeoning influence of emerging markets in Asia, a tectonic shift is taking place in the global security landscape. Asian states are concomitantly arming as their economic clout grows. In light of these developments, security analysts would benefit from a formal means of placing these arms acquisitions in a structural context. Are arms acquisitions on par with the expectations of Asian states, given their structural dispositions, or are recent acquisitions beyond anticipated levels? By using a dynamic panel regression of 187 states from 1950 to 2011, this research predicts arms import volume using the degree of interstate arms linkages, the size of a state's military, and its level of economic development. The technique offers analysts a formal means of distinguishing orthodox behavior in importing conventional weapons from extraneous security motivations. The article concludes by generating near-term forecasts of Asian arms imports and discussing the implications of the technique.     

ANALYSING THE DETERMINANTS OF TERRORISM IN TURKEY USING GEOGRAPHICALLY WEIGHTED REGRESSION

This paper investigates the determinants of provincial terrorism in Turkey taking spatial dimension into account for the time period 1990–2006. Following a traditional global regression analysis, spatial variations in the relationships are examined with geographically weighted regression (GWR) to obtain locally different parameter estimates. Empirical results indicate that increases in income and schooling ratio tend to reduce the provincial average level of terrorism, whereas an increase in unemployment enhances it. Moreover, GWR results indicate that the provincial effects of per capita income and education are more pronounced for the Eastern and South Eastern provinces compared to the Western provinces.     

Economic Performance And Terrorist Activity In Latin America

We investigate the link between economic performance and terrorism for 18 Latin American countries from 1970 to 2007, taking into account the potentially complex nature of this link. Panel causality analysis findings indicate that during this period, terrorism had no causal effect on economic growth. By contrast, we find that growth reduced terrorism in the less developed but not in the higher developed Latin American economies. We argue that group-specific differences (linked to patterns of economic development) govern this causal heterogeneity. From a series of negative binomial regressions we gain additional support for our findings, while also identifying further determinants of terrorism.     

基于统计回归模型的突发事件网络舆情研究

突发事件网络舆情传播速度快,在较短的时间内能够引起社会大众的广泛关注,产生较大的影响.通过利用突发事件网络舆情传播规律和酶促反应相似的机理,构建统计回归模型来研究分析突发事件网络舆情的规律,并通过实例进行论证,以期为政府应对突发事件网络舆情提供参考.     

Sequential learning versus no learning in Bayesian regression models

We consider two regression models: linear and logistic. The dependent variable is observed periodically and in each period a Bayesian formulation is used to generate updated forecasts of the dependent variable as new data is observed. One would expect that including new data in the Bayesian updates results in improved forecasts over not including the new data. Our findings indicate that this is not always true. We show there exists a subset of the independent variable space that we call the “region of no learning.” If the independent variable values for a given period in the future are in this region, then the forecast does not change with any new data. Moreover, if the independent variable values are in a neighborhood of the region of no learning, then there may be little benefit to wait for the new data and update the forecast. We propose a statistical approach to characterize this neighborhood which we call the “region of little learning.” Our results provide insights into the trade‐offs that exist in situations when the decision maker has an incentive to make an early decision based on an early forecast versus waiting to make a later decision based on an updated forecast. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 532–548, 2014