Probability theory in fuzzy sample spaces

This paper tries to develop a neat and comprehensive probability theory for sample spaces where the events are fuzzy subsets of The investigations are focussed on the discussion how to equip those sample spaces with suitable -algebras and metrics. In the end we can point out a unified concept of random elements in the sample spaces under consideration which is linked with compatible metrics to express random errors. The result is supported by presenting a strong law of large numbers, a central limit theorem and a Glivenko-Cantelli theorem for these kinds of random elements, formulated simultaneously w.r.t. the selected metrics. As a by-product the line of reasoning, which is followed within the paper, enables us to generalize as well as to bring together already known results and concepts from literature.Acknowledgement. The author would like to thank the participants of the 23rd Linz Seminar on Fuzzy Set Theory for the intensive discussion of the paper. Especially he is indebted to Professors Diamond and Höhle whose remarks have helped to get deeper insights into the subject. Additionally, the author is grateful to one anonymous referee for careful reading and valuable proposals which have led to an improvement of the first draft.This paper was presented at the 23rd Linz Seminar on Fuzzy Set Theory, Linz, Austria, February 5–9, 2002.     

Robustness issues in multilevel regression analysis

A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub‐units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in general not completely independent. Multilevel analysis software accounts for this dependence and in recent years these programs have been widely accepted. Two problems that occur in the practice of multilevel modeling will be discussed. The first problem is the choice of the sample sizes at the different levels. What are sufficient sample sizes for accurate estimation? The second problem is the normality assumption of the level‐2 error distribution. When one wants to conduct tests of significance, the errors need to be normally distributed. What happens when this is not the case? In this paper, simulation studies are used to answer both questions. With respect to the first question, the results show that a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second‐level standard errors. The answer to the second question is that only the standard errors for the random effects at the second level are highly inaccurate if the distributional assumptions concerning the level‐2 errors are not fulfilled. Robust standard errors turn out to be more reliable than the asymptotic standard errors based on maximum likelihood.     

审计师对舞弊报表说不了吗——来自配样本的初步证据

本文以1998—2005年上市公司舞弊样本与配对样本作为研究对象,实证分析了非标审计意见与舞弊报表的关系。研究结果表明,审计师能够以出具非标审计意见的方式揭露报表舞弊。     

结果质量与过程质量探析

服务营销专家格罗鲁斯认为，服务质量包括两方面内容：结果质量与过程质量。但对于结果质量与过程质量孰轻孰重，是一个在理论上和实际生活中都未能很好解决的问题。文章在回顾相关理论的基础上，认为判断服务结果质量与过程质量孰轻孰重不能一概而论，而应综合考虑行业、顾客和社会文化等因素，将提升服务质量的努力放在不同的方面。     

供应链中基于质量控制的契约模型分析

供应链中的业务外包策略充分发挥了供应链节点企业的核心竞争力，但同时存在产品质量难以控制的问题。本文通过构建以价格折扣和质保金为参数的契约模型，分析了制造商在非对称信息条件下对业务外包产品进行质量控制的最优契约设计问题。     

质量技术监督系统质检机构改革浅议

提出质量技术监督系统所属质检机构改革要顺应国家机关体制改革和国家质量技术监督垂直管理系统改革的大好机遇,摒弃旧观念,丢掉“铁饭碗”,建立新机制,进入大市场,并提出“一所二制”建立学科研究,组建质量技术咨询服务中心和部门“12315”等建议。     

质量心理学引论

文章从顾客满意的质量观，心理因素对质量形成、交换和消费全过程的作用，人们对质量的认知心理以及心理学在质量管理中的应用等多个角度，探讨了质量与心理学的关系，提出了建立新的质量心理学的课题。     

不同企业下物流信息系统的实现

本文首先讨论了不同企业建立物流信息系统的前提要求，然后指出了不同企业下构建物流信息系统的基本原则和模式，并提出了在国内的特殊环境下企业构建物流信息系统的几点建议。     

基于Internet/Intranet的供应链管理信息系统研究

分析了供应链和供应链管理的基础,并针对一个MIS的模块结构实例,给出基于Internet/Intranet的供应链管理信息系统的一个例子。     

通信运营企业的质量管理的再提高

现在通信运营企业为了提高服务质量,不断的加强员工培训,制定客户服务规范,使出了浑身解数,但是仍不见服务质量有显著的提高,用户对企业服务质量的投诉仍居高不下。本文就针对通信运营企业再次提高服务质量的问题,提出了价值链质量管理,鼓励企业实行内部营销,以建立一个完整的质量体系,而不是仅仅关注企业中某一个直接面对客户的职能部门。