R·A·S方法的改进算法

R·A·S 方法是目前世界各国普遍采用的一种修订投入产出模型系统的数学方法.本文以矩阵逼近的角度提出一种改进算法,该算法给出了计算最优代替数和制造乘数的迭代公式,具有很强的可操作性,且能获理想的总体逼近效果.     

林译《迦茵小传》的文学价值及其影响

英国作家哈葛德本是二三流的作家,他的小说<迦茵小传>在其本国并未引起什么反响,但翻译家林纾颇有"化腐朽为神奇"的本领,经他"加工"过的<迦茵小传>虽说与原作出入较大,有失其"真",但此作却取得了极高的艺术成就,成为轰动中国文坛的佳作,在清末文学界引起很大轰动,对中国文学观念的嬗变也产生了积极的影响.因此,特定历史时期出现的林译<迦茵小传>仍具有值得我们研究的文学价值.     

撒拉族研究在国外

海外的撒拉族研究工作虽然由来已久,然而进展缓慢。从20世纪80年代开始,杜安霓和莱茵哈德·韩伦在撒拉族研究方面做了不少工作,他们的研究范围主要集中在撒拉族民俗和语言两个方面。本文对他们的研究成果做了简要评介。     

R～2空间上的可拓集合

本文把R～1上的可拓集合推广到R～2空间上,并在R～2空间上研究了可拓集合之间的关系、性质等.     

Data-Informed Collection Decisions Using R

ABSTRACT

Collection decisions can seem daunting to a librarian who is new to the job. In charge of many new-to-her subject fund codes, the author wanted to understand what had been previously purchased using these funds and how to effectively manage a custom ebook collection. The author used the R programming language to clean and merge purchase and usage data. This article will provide an overview of tasks in R that can be leveraged for making data-informed collection decisions and for sharing information with colleagues.     

Statutory leave entitlements across developed countries: Why US workers lose out on work–family balance

Using a composite index of legal provisions for annual and family leave in western Europe, the United States, Canada, Australia, Japan and the Republic of Korea, the authors rank legislative support for this aspect of work–family balance. The United States ranks last: its employers are not required to grant annual leave and employees can take no more than 12 weeks' family leave per year. The United States' comparatively low labour standards, the authors argue, may be due to the dominance of a market‐based conception of employment and the assumption of equal employer–employee bargaining power, neither of which is fully shared by the other industrialized democracies.     

Decentralization and growth: Do informal institutions and rule of law matter?

We develop a model of the interactions among decentralization, informal institutions and the rule of law. The model sheds light on the ambiguous empirical results reported in the literature regarding the growth effects of the policy of devolving fiscal responsibility to local governments. We find that the distribution of civicness within a country determines the magnitude of the effects of decentralization on its regional convergence, as well as whether decentralization fosters or dampens the country’s national growth. We perform a series of simulated “reforms” using Monte Carlo methods parameterized using OECD countries data set. We then test our findings using a panel data set of 23 OECD countries covering the period 1975–2010. We find that the short and the long run growth effect of decentralization policy depends on the size of the policy reform and can range from extremely negative to positive depending on the rule of law, the level of social capital and its regional dispersion, in line with the model predictions.     

On detecting outliers in the Pareto distribution

In this paper, we introduce two new statistics for detecting outliers in the Pareto distribution. These new statistics are the extension of the statistics for detecting outliers in exponential and gamma distributions. In fact, we compare the power of our test statistics with the other statistics and select the best test statistic for detecting outliers in the Pareto distribution. Finally, numerical examples of different insurance claims are used to see the performance of the test.     

Sir Godfrey Thomson: a statistical pioneer

Summary.  Godfrey Thomson (1881–1955) was a leading figure in intelligence testing who made his name in that field first at Armstrong College, Newcastle, and then at the University of Edinburgh. In the course of his practical work he identified many theoretical problems which were essentially statistical in character. In particular, he used maximum likelihood estimation as early as 1919 and his statistical work largely set the course of modern factor analysis and related techniques. His statistical abilities were recognized, at different stages of his career, by both Karl Pearson and Sir Ronald Fisher. His key insight was to recognize the importance of Fisherian inference for the future of that subject.