Exploring Partial Order of European Countries

Partial Order Theory has been recently more and more employed in applied science to overcome the intrinsic disadvantage hidden in aggregation, if a multiple attribute system is available. Despite its numerous positive features, there are many practical cases where the interpretation of the partial order can be rather troublesome. In these cases the analysis of underlying dimensions could be useful to uncover particular data structures. The paper shows a way of addressing the problem with the help of an actual case study, which deals with European opinions on services of general interest. In particular, a partial order of countries is firstly provided and then a method to detect dimensions is discussed and applied. The analysis stems directly from the Partially Order Set (poset) and Lattice theory with particular references to dimension theory and Formal Concept Analysis. The study is eventually able to pinpoint role and relevance of different attributes characterizing EU countries which are used to define the partial order.

All Data are Wrong,but Some are Useful? Advocating the Need for Data Auditing

In a recent article from the Annals of Applied Statistics, Cox discussed the main phases of applied statistical research ranging from clarifying study objectives to final data analysis and interpreting results. As an incidental remark to these main phases, we advocate that beyond cleaning and preprocessing the data, it is a good practice to audit the data to determine if they can be trusted at all. A case study based on Ghanaian Official Fishery Statistics is used to illustrate this need, with Benford's law being the tool used to carrying out the data audit. Supplementary materials for this article are available online.     

Generalized simultaneous confidence regions for regression coefficients and their ratios

This study constructs a simultaneous confidence region for two combinations of coefficients of linear models and their ratios based on the concept of generalized pivotal quantities. Many biological studies, such as those on genetics, assessment of drug effectiveness, and health economics, are interested in a comparison of several dose groups with a placebo group and the group ratios. The Bonferroni correction and the plug-in method based on the multivariate-t distribution have been proposed for the simultaneous region estimation. However, the two methods are asymptotic procedures, and their performance in finite sample sizes has not been thoroughly investigated. Based on the concept of generalized pivotal quantity, we propose a Bonferroni correction procedure and a generalized variable (GV) procedure to construct the simultaneous confidence regions. To address a genetic concern of the dominance ratio, we conduct a simulation study to empirically investigate the probability coverage and expected length of the methods for various combinations of sample sizes and values of the dominance ratio. The simulation results demonstrate that the simultaneous confidence region based on the GV procedure provides sufficient coverage probability and reasonable expected length. Thus, it can be recommended in practice. Numerical examples using published data sets illustrate the proposed methods.     

Ignorability conditions for frequentist non parametric analysis of conditional distributions with incomplete data

Rubin (1976 Rubin, D.B. (1976). Inference and missing data. Biometrika 63(3):581–592.[Crossref], [Web of Science ®] , [Google Scholar]) derived general conditions under which inferences that ignore missing data are valid. These conditions are sufficient but not generally necessary, and therefore may be relaxed in some special cases. We consider here the case of frequentist estimation of a conditional cdf subject to missing outcomes. We partition a set of data into outcome, conditioning, and latent variables, all of which potentially affect the probability of a missing response. We describe sufficient conditions under which a complete-case estimate of the conditional cdf of the outcome given the conditioning variable is unbiased. We use simulations on a renal transplant data set (Dienemann et al.) to illustrate the implications of these results.     

人口受教育程度对居民消费的影响研究--基于动态面板分位数回归法

本文采用1996～2015年的31个省市自治区宏观数据,使用动态面板分位数回归方法研究人口受教育程度和居民消费的数量关系。研究结果表明,人口受教育程度对消费有着较为显著的影响,且人口受教育程度对中等消费支出水平的影响略大于高、低消费支出水平的影响,这说明中等消费水平的居民消费受到人口受教育程度的影响更加敏感和有弹性。另外,模型中其他各解释变量系数在各分位数上的差异不大,说明这些影响因素对于不同消费支出水平的消费影响没有太明显差异。     

The social benefits of water and sanitation projects in Northern Colombia: Cost‐Benefit Analysis,the Water Poverty Index and beyond

Water, and Sanitation projects play an important role in alleviating water‐related poverty in developing countries. There are several methodologies that will not only assess the performance of these projects but will also help to better identify the problem and its characteristics, and to improve the efficiency of the investment. The Water Poverty Index is one of them. It is a very useful tool that helps to measure water stress at the household level in a holistic way, and to identify priorities. It is important, however, to complement it with a Cost‐Benefit Analysis that will take into account the costs of the project. A case study in Northern Colombia illustrates this point.     

网络犯罪对刑事侦查的挑战与应对

网络犯罪不仅包括以网络为对象的犯罪,也包括以网络为工具的犯罪。由于网络犯罪具有犯罪主体、手段的专业化、犯罪客体的多样化、犯罪行为的隐蔽化的特点,导致犯罪行为扩散迅速、身份易藏、证据有限、毁证容易、适用法律困难等问题,对刑事侦查带来了极大的挑战。这种挑战主要体现在管辖权、证据制度和侦查措施三个方面。我国应对网络犯罪的侦查制度较为落后,应当从专门侦查机构、管辖制度、电子数据收集制度和技术侦查措施四个方面来加以完善。     

高职《食品分析》课程实践教学改革的探讨

《食品分析》实践教学是食品专业的一个重要的教学环节,为培养创新人才、提高学生的实验动手能力,我们在《食品分析》实验课教学中,通过从实验教学的课程体系、教学方法和手段、考核方式等方面进行了探讨,对课程实践教学改革做了一些有益的尝试和探索。     

基于成本-效益分析的企业技术创新策略选择

以企业效益最大化为分析前提,假设企业仅面临着开展或不开展技术创新两种策略选择,并假设两种策略的收入与成本函数均为线性函数.在此基础上运用成本-效益分析法,讨论在不同情况下企业的技术创新策略选择及其判别条件,并对分析结论的经济涵义给予解释.     

房地产预警指标体系及综合预警方法研究

 本文在对常用的房地产预警指标体系选择方法及筛选流程进行分析的基础上,提出了一种主、客观相结合的房地产预警指标选择方法,即先采用综合分析方法对指标进行初次选择,然后用聚类分析和相关分析相结合的方法进行再次选择。最后建立了武汉市房地产预警指标系统,并进行了房地产综合预警。