论数+形+量结构中量词的特点

现代汉语中数量词中插入形容词的结构即数+形+量结构比较常见,例如,一大张纸,一大群人,一长根铁丝。陆俭明先 生对这种语言现象进行了详细的描写,但是还是留下了一些问题给大家讨论。例如,为什么数量词中间可以插入形容词,插入 形容词对整个结构有什么影响。从量词的特点和形容词的语义指向来看,数+形+量结构中的量词具有空间性、物质性、描写 性的特点,使得形容词能够进入该结构,形容词的语义指向不同,使整个结构有不同的表达功能。     

Visualization for Large‐scale Gaussian Updates

In geostatistics and also in other applications in science and engineering, it is now common to perform updates on Gaussian process models with many thousands or even millions of components. These large‐scale inferences involve modelling, representational and computational challenges. We describe a visualization tool for large‐scale Gaussian updates, the ‘medal plot’. The medal plot shows the updated uncertainty at each observation location and also summarizes the sharing of information across observations, as a proxy for the sharing of information across the state vector (or latent process). As such, it reflects characteristics of both the observations and the statistical model. We illustrate with an application to assess mass trends in the Antarctic Ice Sheet, for which there are strong constraints from the observations and the physics.     

空间面板杜宾模型的Bootstrap Wald-COMFAC检验研究

将共同因子约束(COMFAC)的Wald检验问题引入到空间面板模型中,讨论空间面板杜宾模型与空间面板误差模型的识别问题。蒙特卡洛模拟表明:在有限样本下,基于渐近临界值的Wald检验有着良好的检验功效,但存在着较为严重的尺度扭曲。进一步采用残差Bootstrap方法,在不损失检验功效的前提下,能够显著地降低检验的尺度扭曲。因此,残差Bootstrap方法是更为有效的检验方法。     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

计量经济分析何以滥用——知识结构和学术风气的剖析

现代经济学的实证分析和计量应用变得越来越媚俗化和庸俗化,大多数文章甚至都经不起150年前穆勒所提出的证实规则之逻辑检视,更不要说揭示出经济现象间的因果关系。这一学术取向主要源于两大原因:首先,研究者知识结构的日益狭隘,使得他们局限于事物的表象解释而无法深入揭示事物的本质;其次,学术界功利主义的日益盛行,使得他们以商人心态偏向容易被接受的主流化研究方法。当前,上述两大因素还有深厚的社会环境基础,这使得计量实证分析的形式化和庸俗化倾向更为严重。实际上,众多经济学人在对现实社会环境还缺乏基本理解的情况下就牵强附会地运用计量软件来寻找各变量之间的关系,以至所得出的结果根本无助于现象预测或实践指导,由此也就呈现出明显的“下不着地”之风气。     

熟人社会的空间秩序

村落空间是村落历史的沉淀。不同于熟人社会研究的关系取向,文章引入空间生产的视角,基于南方村落的空间图式阐释熟人社会的空间秩序。“祠堂-阳宅”的平面空间图式定义了农民自然生命升华为道德生命的日常生活路径,而“庙宇-阴宅”的纵向空间图式定义了农民应对日常生活断裂的宗教生活模式。风水的空间实践镶嵌于村落空间图式,并促进了村落空间的结构化和立体化,而村落空间结构维系了村落熟人社会的公共性。可见,熟人社会的空间秩序是空间结构生产与社会秩序生产的统一。现代性力量逐渐抽离了村落空间的历史底蕴,释放了空间权利意识,导致村落空间结构破裂与熟人社会秩序紊乱。立足村庄社会的空间治理是突破村庄空间利益困局,实现乡村振兴的重要方式。     

数字经济对地区经济增长差距的影响效应分析

文章基于中国30个省(自治区、直辖市)2013—2020年的面板数据,运用固定效应模型、中介效应模型和空间杜宾模型等方法,实证检验了数字经济发展对地区经济的影响及其传导机制。研究发现,数字经济发展显著缩小地区之间的经济差距,运用工具变量估计、替换被解释变量、剔除异常值等方法进行稳健性检验后,该结论依然成立。中介效应分析表明,地区创新能力是数字经济缩小地区间经济差距的重要机制。空间溢出效应分析表明,数字经济发展对地区间经济增长差距的缩小具有负向的溢出效应。空间异质性分析表明,东部地区数字经济发展对地区经济增长差距的缩小效应相比中部、西部地区更显著;南、北方的异质性分析表明,北方地区数字经济发展对地区经济增长差距的缩小的积极作用比南方更显著。最后,为我国发展数字经济、充分发挥数字经济对缩小地区经济差距,实现地区间协调发展提出政策建议。     

On Bartlett correction of empirical likelihood for regularly spaced spatial data

Bartlett correction constitutes one of the attractive features of empirical likelihood because it enables the construction of confidence regions for parameters with improved coverage probabilities. We study the Bartlett correction of spatial frequency domain empirical likelihood (SFDEL) based on general spectral estimating functions for regularly spaced spatial data. This general formulation can be applied to testing and estimation problems in spatial analysis, for example testing covariance isotropy, testing covariance separability as well as estimating the parameters of spatial covariance models. We show that the SFDEL is Bartlett correctable. In particular, the improvement in coverage accuracies of the Bartlett‐corrected confidence regions depends on the underlying spatial structures. The Canadian Journal of Statistics 47: 455–472; 2019 © 2019 Statistical Society of Canada