Partial Round-Robin Comparisons with Perfect Rankings

In teaching the development of uniformly most powerful unbiased (UMPU) tests, one rarely discusses the performance of alternative biased tests. It is shown, through the comparison of two independent Bernoulli proportions, that a biased test (the Z test) can be more powerful than the UMPU test (Fisher's exact test—randomized) in a large region of the alternative parameter space. A more general example is also given.     

采用汇文系统实现统编工作的探索

统一编目工作是促进图书馆业务工作自动化与标准化的重要技术,笔者从采用汇文系统进行统编工作的实践尝试中,探讨了统编工作中存在的一些实际问题,并提出了解决方案.     

A generalized scheffé method of multiple comparisons

Standard resulrs on the extrema of quotients of quadratic forms are extended to the non-negative definite case. The maximum and the set over which it is achieved are characterized explicitly both in terms of generalized inverse matrices and generalized eigenvalues. These results become the basis of Scheffe type multiple comparisons in the usual way. To demonstrate their application to statistics with singular covariance matrices, the method is detailed for Mantel-Haenszel, Breslow, and Cox statistics. An example is presented illustrating a situation where the proposed Scheffe type comparisons may be better than the pairwise method.     

聚酯聚合反应分层温度控制系统

介绍了聚合釜聚酯聚合反应温度过程控制系统及其设计方法。控制系统以高性能单片机系统为直控机（DDC）,PC机为上位机（MIS）,直控机负责聚合釜釜温及夹套温度采集、分析、控制,并将参数送上位机。上位机负责控制过程的时间及温度值设定、温度监测、工作过程监控。     

纸机传动控制系统的配置与控制原理

针对纸机日益高速化、大型化的发展趋势，设计了国内先进的全数字化通信网络传动控制系统。系统采用西门子全集成自动化解决方案，实现造纸生产过程的全线集成控制。高可靠的工业现场总线PROFIBUS—DP和工业以太网相结合的网络结构，有效地实现了数据共享，全面解决了整个工艺过程的控制。文章详细介绍了控制系统的配置和功能，重点阐述了速度链、负荷分配控制、张力控制等主要控制原理。     

Editor's Report

There are two common methods for statistical inference on 2 × 2 contingency tables. One is the widely taught Pearson chi-square test, which uses the well-known χ²statistic. The chi-square test is appropriate for large sample inference, and it is equivalent to the Z-test that uses the difference between the two sample proportions for the 2 × 2 case. Another method is Fisher’s exact test, which evaluates the likelihood of each table with the same marginal totals. This article mathematically justifies that these two methods for determining extreme do not completely agree with each other. Our analysis obtains one-sided and two-sided conditions under which a disagreement in determining extreme between the two tests could occur. We also address the question whether or not their discrepancy in determining extreme would make them draw different conclusions when testing homogeneity or independence. Our examination of the two tests casts light on which test should be trusted when the two tests draw different conclusions.     

Fast and Stable Algorithms for Computing and Sampling From the Noncentral Hypergeometric Distribution

Although the noncentral hypergeometric distribution underlies conditional inference for 2 × 2 tables, major statistical packages lack support for this distribution. This article introduces fast and stable algorithms for computing the noncentral hypergeometric distribution and for sampling from it. The algorithms avoid the expensive and explosive combinatorial numbers by using a recursive relation. The algorithms also take advantage of the sharp concentration of the distribution around its mode to save computing time. A modified inverse method substantially reduces the number of searches in generating a random deviate. The algorithms are implemented in a Java class, Hypergeometric, available on the World Wide Web.     

基于公文筐测验的商业银行高层管理人员选拔研究——以商业银行高级人力资源经理岗位为例

在采用文献法、访谈法和问卷法确定了银行高级人力资源管理者胜任力模型的基础上,设计和编制了适用于商业银行人力资源经理岗位的公文筐测验。研究结果表明,公文筐测验具有较高的信度和效度,可以在商业银行高层管理人员选拔中使用,但公文筐测验必须根据具体岗位来设计题目才能保证选拔的效果。     

合理产业结构的确定方法及预测

本文根据技术矩阵决定产业结构的思想,给出了不编制投入产出表年份的合理产业结构的计算方法及预测未来年份合理产业结构的计算方法。并将该方法应用于山东省合理产业结构的确定及预测。方法简便,易操作,且计算结果较理想。     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.