网上组织概说

网上社会组织是在网络社会出现的时候才形成的组织,是一种新型的、特殊的组织.在网上社会中存在的企业、机构如网上政府、网上学校、网上商店等都是网上社会组织的具体形式.本文从社会学角度探讨了网上组织的概念、结构特点和分类.     

宪法对人的分类——基于中国17部宪法文本的分析

自清末仿行立宪以来,宪法在中国的发展已历时百年之久,先后共有17部宪法或宪法性文件相继面世。通过对全部宪法文本进行实证分析,可以抽取出中国宪法对人所作的三大分类即政治分类、法律分类和特殊分类。这三大分类虽然在中国宪法之中都有,但其地位并不相同。并且三大分类下的各项子类之间亦经历了先后不同的出场顺序,从"君上-臣民"到"人民-敌人"、从"国民-非国民"到"公民-非公民"、从"生理因素"到"民族因素"再到"职业及风险因素",各项子类间的接续展开体现出历史清晰的逻辑维度。那就是此三大分类方式所蕴含的"内部迭代更替、外部重点转移"的演变规律及其背后所体现的"政治法-法律法-福利法"的宪法理念乃至"不平等-形式平等-实质平等"的法理念的深刻变迁。     

Sample-size requirements for confidence bounds of prescribed precision on multivariate normal quantiles

The problem of setting confidence bounds on a central multivariate normal quantile is considered. It is shown that for the setting of exact confidence bounds of specified closeness to the quantile,the required minimum size of a normal sample is large and rises rapidly with the number of variates considered.     

A prior-valued estimator applied to multinomial classification

A multinomial classification rule is proposed based on a prior-valued smoothing for the state probabilities. Asymptotically, the proposed rule has an error rate that converges uniformly and strongly to that of the Bayes rule. For a fixed sample size the prior-valued smoothing is effective in obtaining reason¬able classifications to the situations such as missing data. Empirically, the proposed rule is compared favorably with other commonly used multinomial classification rules via Monte Carlo sampling experiments     

Covariate classification by post–stratification

The plug–in Anderson's covariate classification statistic is constructed on the basis of an initially unclassified training sample by means of posty–stratification. The asymptotic efficiency relative to the discriminant based on an initially classified training sample is evaluated for the case where a covariate is present. Effect of post–stratification is examined.     

Nenparametric Two-Group Classification: Concepts and a SAS-Based Software Package

This article introduces BestClass, a set of SAS macros, available in the mainframe and workstation environment, designed for solving two-group classification problems using a class of recently developed nonparametric classification methods. The criteria used to estimate the classification function are based on either minimizing a function of the absolute deviations from the surface which separates the groups, or directly minimizing a function of the number of misclassified entities in the training sample. The solution techniques used by BestClass to estimate the classification rule use the mathematical programming routines of the SAS/OR software. Recently, a number of research studies have reported that under certain data conditions this class of classification methods can provide more accurate classification results than existing methods, such as Fisher's linear discriminant function and logistic regression. However, these robust classification methods have not yet been implemented in the major statistical packages, and hence are beyond the reach of those statistical analysts who are unfamiliar with mathematical programming techniques. We use a limited simulation experiment and an example to compare and contrast properties of the methods included in Best-Class with existing parametric and nonparametric methods. We believe that BestClass contributes significantly to the field of nonparametric classification analysis, in that it provides the statistical community with convenient access to this recently developed class of methods. BestClass is available from the authors.     

农村生活垃圾分类长效机制建设的社会基础

农村生活垃圾分类长效机制建设是农村人居环境整治的重要组成部分。当前生活垃圾分类越来越成为全国各地农村尝试开展与实施的环境治理工程,但如何建立生活垃圾分类长效机制一直困扰着地方环境管理者。通过对浙北码头村的案例分析,总结该村在长期实践中的生活垃圾分类长效机制建设经验以及不同主体参与垃圾分类的行动策略,并深入挖掘长效机制建设背后的社会结构、社会关系、地方文化等社会基础。最后,基于理论分析与实地调查的互动联系,提出内生于村庄社会的制度规范、有效的组织动员结构、可适用的治理技术手段是当前农村生活垃圾分类长效机制建设的社会基础,以此来反映符合农村社会特点、多元主体共同参与的环境治理长效机制建设的理论内涵与实践路径。     

Segmentation,alignment and statistical analysis of biosignals with application to disease classification

We present a novel methodology for a comprehensive statistical analysis of approximately periodic biosignal data. There are two main challenges in such analysis: (1) the automatic extraction (segmentation) of cycles from long, cyclostationary biosignals and (2) the subsequent statistical analysis, which in many cases involves the separation of temporal and amplitude variabilities. The proposed framework provides a principled approach for statistical analysis of such signals, which in turn allows for an efficient cycle segmentation algorithm. This is achieved using a convenient representation of functions called the square-root velocity function (SRVF). The segmented cycles, represented by SRVFs, are temporally aligned using the notion of the Karcher mean, which in turn allows for more efficient statistical summaries of signals. We show the strengths of this method through various disease classification experiments. In the case of myocardial infarction detection and localization, we show that our method compares favorably to methods described in the current literature.