New imperfect rankings models for ranked set sampling

Ranked set sampling is a sampling approach that leads to improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. This ranking may be done either by subjective judgment or according to an auxiliary variable, and it need not be completely accurate. In fact, results in the literature have shown that no matter how poor the quality of the ranking, procedures based on ranked set sampling tend to be at least as efficient as procedures based on simple random sampling. However, efforts to quantify the gains in efficiency for ranked set sampling procedures have been hampered by a shortage of available models for imperfect rankings. In this paper, we introduce a new class of models for imperfect rankings, and we provide a rigorous proof that essentially any reasonable model for imperfect rankings is a limit of models in this class. We then describe a specific, easily applied method for selecting an appropriate imperfect rankings model from the class.     

发展和完善南宁市新型农村合作医疗制度研究

近年来,党和政府一直把发展和完善新型农村合作医疗制度作为改善民生的一项重要工作。南宁市新型农村合作医疗尽管有了较大的发展,但与同为西部省会城市的西安、昆明、成都相比还存在不少差距。这就需要结合地方实际,从运行机制、资金监管、信息化管理、人才建设等软、硬件方面破解新型农村合作医疗制度发展和完善的难题。     

人力资源系统与公司绩效关系研究的测量误差：随机误差与系统误差

文章对人力资源系统与公司绩效关系实证研究中影响其效应大小的围绕项目、时间和评定者测量信度的随机误差和系统误差进行评述与分析,介绍基于构思水平理解评定者间信度的思路,较好解决了实证中的数据来源问题.并提出对管理感知这一新的系统误差的研究.总结改善信度的方法和探索,讨论了人力资源系统测量中需要改进的地方和进一步的研究方向.     

宜昌城市景观概念设计探析

通过分析宜昌市自然地理和人文地理环境,对宜昌城市形象进行了定位,提出了建设具有“山水园林特色的水电旅游城市”的理念,探讨了宜昌城市规划合理利用地理环境的方略与措施。     

从会计角度看中国企业应对反倾销的对策

我国是目前国际上遭受反倾销调查最多的国家,国外反倾销给我国出口贸易造成了巨大损失。会计是反倾销诉讼中的一个关键因素。文章针对会计在我国企业应对反倾销中的作用和我国企业应对反倾销中存在的会计问题进行了分析,在此基础上提出了我国企业从会计角度应对反倾销的对策与建议。     

法国会计模式的分析及对中国的借鉴

该文分析了法国会计模式的特点和影响因素 ,认为法国会计模式与中国会计模式存在相似之处 ,并借鉴法国 1 996年以来会计改革的经验 ,对完善我国会计模式提出了建议     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

Consistent maximum likelihood estimation of a unimodal density using shape restrictions

It is well known that the unimodal maximum likelihood estimator of a density is consistent everywhere but at the mode. The authors review various ways to solve this problem and propose a new estimator that is concave over an interval containing the mode; this interval may be chosen by the user or through an algorithm. The authors show how to implement their solution and compare it to other approaches through simulations. They show that the new estimator is consistent everywhere and determine its rate of convergence in the Hellinger metric.