走出离合词现象的迷局——从准定语与准名词现象入手

词的离合现象是语法研究中的老大难问题。文章独辟蹊径,从与词的离析使用密切相关的准定语和准名词入手,根据句子的信息结构原则,解释了词的离合现象的成因。文章指出,话语的信息结构安排是促成句法结构重组的语用推力:话题语的指称化处置诱发了准定语结构的操作,基本不涉及词的离析;评述语受单一新信息原则制约而出现的事件化处置,迫使及物性低的双音词强行拆分以容纳事件化所需要素,是诱发词的离析并催生准定语和准名词的直接动因。文章的观点是,用朴素的眼光看汉语,汉语中并无什么离合词,词的离合只是非形态语中双音词的用法变异,是语用型语言句子信息结构进行整编与重组的副产品。     

社会保险制度中社会风险的产生、内涵及应对*

社会风险是社会保险制度存在的前提，没有社会风险，就不会有社会保险。从风险、风险社会及社会风险这些相关概念出发，全面阐述社会保险制度中社会风险的产生及其发展路径，发现社会保险制度上所应对的社会风险是一种中度危险的风险事故，有着自身特定的制度内涵。在此基础上，从作为技术途径的保险原则与社会保险权之权利规则的两个视角分析了社会保险制度对社会风险的应对原理、机制与优势。     

试述马克思主义大众化传播应坚持的四个基本原则

马克思主义大众化传播是一个极其复杂、艰巨的社会系统工程。学术界对于马克思主义大众化传播应该坚持什么样的原则和方法也是众说纷纭。从当代世界和中国现实情况、信息传播技术飞速发展的时代背景,以及马克思主义理论创新发展的内在规律出发,马克思主义大众化传播应坚持指导思想一元和文化价值多元的统一、理论逻辑和生活逻辑的统一、科学性与通俗性的统一、传承性和开放性的统一等四个基本原则。     

Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects

Multiple imputation (MI) is an appealing option for handling missing data. When implementing MI, however, users need to make important decisions to obtain estimates with good statistical properties. One such decision involves the choice of imputation model–the joint modeling (JM) versus fully conditional specification (FCS) approach. Another involves the choice of method to handle interactions. These include imputing the interaction term as any other variable (active imputation), or imputing the main effects and then deriving the interaction (passive imputation). Our study investigates the best approach to perform MI in the presence of interaction effects involving two categorical variables. Such effects warrant special attention as they involve multiple correlated parameters that are handled differently under JM and FCS modeling. Through an extensive simulation study, we compared active, passive and an improved passive approach under FCS, as JM precludes passive imputation. We additionally compared JM and FCS techniques using active imputation. Performance between active and passive imputation was comparable. The improved passive approach proved superior to the other two particularly when the number of parameters corresponding to the interaction was large. JM without rounding and FCS using active imputation were also mostly comparable, with JM outperforming FCS when the number of parameters was large. In a direct comparison of JM active and FCS improved passive, the latter was the clear winner. We recommend improved passive imputation under FCS along with sensitivity analyses to handle multi-level interaction terms.     

Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science

ABSTRACT

Scientific research of all kinds should be guided by statistical thinking: in the design and conduct of the study, in the disciplined exploration and enlightened display of the data, and to avoid statistical pitfalls in the interpretation of the results. However, formal, probability-based statistical inference should play no role in most scientific research, which is inherently exploratory, requiring flexible methods of analysis that inherently risk overfitting. The nature of exploratory work is that data are used to help guide model choice, and under these circumstances, uncertainty cannot be precisely quantified, because of the inevitable model selection bias that results. To be valid, statistical inference should be restricted to situations where the study design and analysis plan are specified prior to data collection. Exploratory data analysis provides the flexibility needed for most other situations, including statistical methods that are regularized, robust, or nonparametric. Of course, no individual statistical analysis should be considered sufficient to establish scientific validity: research requires many sets of data along many lines of evidence, with a watchfulness for systematic error. Replicating and predicting findings in new data and new settings is a stronger way of validating claims than blessing results from an isolated study with statistical inferences.     

Simultaneous edit-imputation and disclosure limitation for business establishment data

Business establishment microdata typically are required to satisfy agency-specified edit rules, such as balance equations and linear inequalities. Inevitably some establishments' reported data violate the edit rules. Statistical agencies correct faulty values using a process known as edit-imputation. Business establishment data also must be heavily redacted before being shared with the public; indeed, confidentiality concerns lead many agencies not to share establishment microdata as unrestricted access files. When microdata must be heavily redacted, one approach is to create synthetic data, as done in the U.S. Longitudinal Business Database and the German IAB Establishment Panel. This article presents the first implementation of a fully integrated approach to edit-imputation and data synthesis. We illustrate the approach on data from the U.S. Census of Manufactures and present a variety of evaluations of the utility of the synthetic data. The paper also presents assessments of disclosure risks for several intruder attacks. We find that the synthetic data preserve important distributional features from the post-editing confidential microdata, and have low risks for the various attacks.     

行政裁量的界限与公共利益

公共利益是贯穿于整个公法领域的重要法律概念,为所有国家行为提供合法性理由。但由于公共利益概念的不确定性,在解释其内容时往往要诉诸于行政裁量。当今宪政国家要求行政裁量行为遵守内外界限,而此种界限映射到公共利益上即表现为法定性以及合理性和程序性。     

公平合理利用原则在国际水法中的适用局限及其反思

作为国际水法的基本原则,公平合理利用原则具有条约法及习惯法的规范基础,并且可指导跨界水道的利用及水资源分配,但是其抽象性及模糊性降低了适用的确定性。公平合理利用原则适用的局限性主要表现为理论基础不成熟,概念难以统一明确,非排他性考虑因素多元但无优先次序等方面。对公平合理利用原则的反思要求充分尊重跨界水道的自然特性,遵从不造成重大损害原则及国际合作原则,厘清相关考虑因素的优先次序,推动建立水道国家的利益共识,从而实现流域国家对跨界水道的公平合理利用。      

论量刑事实的证明责任

刑事证明责任内涵应当包含“评价要素”,对于特定事实,证明责任承担者所举证据仅需要达到说服法官相信事实可能成立之程度。分配证明责任应当遵循证据所持、证据距离与诉讼效率等原则。我国刑事诉讼法未将量刑事实的证明责任进行区别对待,对公诉机关和法院的证明责任规定也不尽合理,更未规定证明责任转移规则。为最大程度实现量刑公正,应尝试对我国量刑事实的证明责任进行体系性完善,合理规定控方、被告人与被害人的证明责任,对立功、认罪认罚等量刑事实创设证明责任转移规则。     

Skew-normal factor analysis models with incomplete data

Traditional factor analysis (FA) rests on the assumption of multivariate normality. However, in some practical situations, the data do not meet this assumption; thus, the statistical inference made from such data may be misleading. This paper aims at providing some new tools for the skew-normal (SN) FA model when missing values occur in the data. In such a model, the latent factors are assumed to follow a restricted version of multivariate SN distribution with additional shape parameters for accommodating skewness. We develop an analytically feasible expectation conditional maximization algorithm for carrying out parameter estimation and imputation of missing values under missing at random mechanisms. The practical utility of the proposed methodology is illustrated with two real data examples and the results are compared with those obtained from the traditional FA counterparts.