肯尼迪和约翰逊政府对西欧的经济政策

50年代末60年代初,西欧独立性增强,对美国的主导地位形成挑战。肯尼迪和约翰逊两届政府提出并实施了对西欧新的政策:建立伙伴关系的大西洋共同体。目的是将发展起来的西欧规制到美国主导的大西洋框架内。政策体现了美国门户开放的扩张方式和美国冷战战略的需要,并反映了美欧经济关系由依附趋向平等的走向。     

人情味的缺乏：约翰逊论弥尔顿人格与作品的缺憾

塞缪尔·约翰逊在《诗人传》中试图建立和呈现作家的性格、心智或才华与其诗歌风貌之间的关系.以《弥尔顿传》为例,约翰逊在这篇传记中塑造了一个傲岸不群、恃才自负的诗人形象.约翰逊认为弥尔顿这样的人格属性既成就了他诗歌独有的特色,如充满原创性的崇高想象,但同时又造成他作品的一个巨大缺憾：“人情味的缺乏”.文章着重研究约翰逊如何使这一缺陷与弥尔顿的性情和才华有机地联系起来,并将《弥尔顿传》放在18世纪以及后世的批评史中,考察约翰逊作为优秀批评家的独到见地和长远影响.     

约翰逊与美国的教育平等化改革

约翰逊总统60年代的社会改革是美国联邦教育政策形成的关键期,通过广泛的教育立法,对各级学校及贫困学生进行了大量的资助,美国联邦奠定了教育平等化的发展方向,促进了教育事业的发展及社会文明的整体提高。其以教育作为解决社会贫困及公平问题的重要手段,值得进行深刻的历史思考。     

灵魂的冲突——鲍斯威尔《约翰生传》研究

鲍斯威尔的《约翰生传》通过人物同环境的冲突以及轶事和对话来展示性格。因此 ,鲍斯威尔以其艺术的眼光遴选了约翰生与环境、他人以及自身性格的冲突 ,展示了约翰生独特的个性。以灵魂的冲突为基点 ,鲍斯威尔的《约翰生传》为研究、写作传记提供了一种新的思路     

基于混合流水作业组织的集装箱码头装卸作业集成调度优化

集装箱码头集疏运资源调度的对象是由岸桥、集卡、场桥所构成的多阶段一体化的集装箱装、卸、运操作系统,将该系统的调度优化基于多阶段混合流水线调度问题建立混合整数规划模型,同时考虑集装箱码头现实作业中预定义顺序、避免岸桥交叉作业、以及取决于作业顺序的切换时间等现实约束,针对问题自身的特点设计了两阶段启发式算法,得出各阶段设备的指派结果及作业顺序。通过与基于现行调度规则的调度方案以及与目标函数理论下界值的对比实验,显示了所提出的集成调度模型及求解算法能够有效降低船舶在港时间并实现集卡资源的共享,为集装箱码头集疏运资源的集成调度提供了新的思路。     

Testing Goodness of Fit for Parametric Families of Copulas—Application to Financial Data

ABSTRACT

This article suggests a chi-square test of fit for parametric families of bivariate copulas. The marginal distribution functions are assumed to be unknown and are estimated by their empirical counterparts. Therefore, the standard asymptotic theory of the test is not applicable, but we derive a rule for the determination of the appropriate degrees of freedom in the asymptotic chi-square distribution. The behavior of the test under H ₀ and for selected alternatives is investigated by Monte Carlo simulation. The test is applied to investigate the dependence structure of daily German asset returns. It turns out that the Gauss copula is inappropriate to describe the dependencies in the data. A t _ν-copula with low degrees of freedom performs better.     

Approximations to permutation distributions in the completely randomized design

The permutation distribution of a statistic T equivalent to the usual F ratio for the completely randomized design is considered. A correction to the second moment of T derived by Robinson (1983) is presented and the third and fourth moments are educed. Inadequacies in the conventional permutation distribution approximations are demonstrated.     

A tool for systematically comparing the power of tests for normality

In this article, we describe a new approach to compare the power of different tests for normality. This approach provides the researcher with a practical tool for evaluating which test at their disposal is the most appropriate for their sampling problem. Using the Johnson systems of distribution, we estimate the power of a test for normality for any mean, variance, skewness, and kurtosis. Using this characterization and an innovative graphical representation, we validate our method by comparing three well-known tests for normality: the Pearson χ² test, the Kolmogorov–Smirnov test, and the D'Agostino–Pearson K ² test. We obtain such comparison for a broad range of skewness, kurtosis, and sample sizes. We demonstrate that the D'Agostino–Pearson test gives greater power than the others against most of the alternative distributions and at most sample sizes. We also find that the Pearson χ² test gives greater power than Kolmogorov–Smirnov against most of the alternative distributions for sample sizes between 18 and 330.     

Comparisons of the percentage points of distributions with the same first four moments,chosen from eight different systems of frequency curves

Probability integrals and percentage points of univariate distributions from up to eight different families, having common first four moments are compared. Among interesting observations is the remarkable consistency in the standardized upper and lower 5% points over considerable regions of the √β₁, β₂ plane; also the closeness of agreement between the log-normal and non-central t distributions and the Pearson Type VI and Type IV curves respectively.     

A practical method to calculate probabilities: illustrative example from the electronic industry business

The real-life environment is made of probabilistic data by nature and the ability to make decisions based on probabilities is crucial in the business world. It is common to have a set of data and the need of calculating the probability of taking a value greater or less than a specific value. It is also common in many companies the unavailability of a statistical software or a specialized professional in statistics. The purpose of this paper is to present a practical and simple method to calculate probabilities from normal or non-normal distributed data set and illustrate it with an application from the electronic industry. The method does not demand statistical knowledge from the user; there is no need of normality assumptions, goodness test or transformations. The proposed method is easy to implement, robust and the experiments have evidenced its quality. The technique is validated with a large variety of instances and compared with the well-known Johnson system of distributions.