Approximation of density functions by orthogonal series with grouped data

Summary Letg(x) andf(x) be continuous density function on (a, b) and let {ϕ_j} be a complete orthonormal sequence of functions onL ₂(g), which is the set of squared integrable functions weighted byg on (a, b). Suppose that over (a, b). Given a grouped sample of sizen fromf(x), the paper investigates the asymptotic properties of the restricted maximum likelihood estimator of density, obtained by setting all but the firstm of the ϑ_j’s equal to0. Practical suggestions are given for performing estimation via the use of Fourier and Legendre polynomial series. Research partially supported by: CNR grant, n. 93. 00837. CT10.     

Sampling from spatial databases

This paper deals with techniques for obtaining random point samples from spatial databases. We seek random points from a continuous domain (usually ²) which satisfy a spatial predicate that is represented in the database as a collection of polygons. Several applications of spatial sampling (e.g. environmental monitoring, agronomy, forestry, etc) are described. Sampling problems are characterized in terms of two key parameters: coverage (selectivity), and expected stabbing number (overlap). We discuss two fundamental approaches to sampling with spatial predicates, depending on whether we sample first or evaluate the predicate first. The approaches are described in the context of both quadtrees and R-trees, detailing the sample first, acceptance/rejection tree, and partial area tree algorithms. A sequential algorithm, the one-pass spatial reservoir algorithm is also described. The relative performance of the various sampling algorithms is compared and choice of preferred algorithms is suggested. We conclude with a short discussion of possible extensions.     

Estimation for Continuous Branching Processes

The maximum-likelihood estimator for the curved exponential family given by continuous branching processes with immigration is investigated. These processes originated from population biology but also model the dynamics of interest rates and development of the state of technology in economics. It is proved that in contrast to branching processes with discrete space and/or time the MLE gives a unified approach to the inference. In order to include singular subdomains of the parameter space we modify the MLE slightly. Consistency and asymptotic normality for the MLE are considered. Concerning the asymptotic theory of the experiments, all three properties LAQ, LAN, and LAMN occur for different submodels     

我国制造业产能利用率测度与影响因素研究——基于十个重点行业

选取制造业的十个重点行业,使用2001-2014年的面板数据,运用数据包络分析法(DEA)对其产能利用率、技术效率和设备利用率进行了测算,结果显示:我国制造业的部分行业存在严重的产能过剩问题;非金属矿物制品业、黑色金属冶炼和压延加工业等传统行业的产能利用率较低;通信设备、计算机及其他电子设备制造业等新兴行业的产能利用率相对较高.剖析制造业产能过剩的影响因素,发现市场需求与产能利用率存在显著的正相关关系,资本密集度、就业水平、开放度、金融支持与产能利用率有显著的负相关关系.     

当前大学生对国学教育的认知和需求——以某高校调查数据为例

对大学生国学认知程度和需求的调查是有效开展国学教育的基础。这个基础必须通过详实的数据调查和实证分析来奠定。通过在某高校展开问卷调查,我们发现大学生对于国学的不甚了解是一个普遍现象。高校学生对国学教育的需求不仅存在,而且是理性而合理的。真正影响高校国学教育效果的症结仍然是师资力量、课程内容、教学步骤等问题。我们建议应该遵循由易到难、从日常习用到弘文奥义、分类教学的原则,系统地做好国学教育内容的顶层设计。     

Multiscale Combined Forecast of Carbon PriceBased on Mixed Structure Data

Accurate forecast of the carbon trading price is of great significance in promoting the scientific and rational development of carbon trading market. Therefore, this paper proposes a multi-scale combined forecasting method for carbon price based on mixed structure data. First, the Google Index is used to extract the unstructured data related to the carbon price.The dimensions of unstructured data are reduced based on principal component analysis. Then, EMD is employedto the structured data,unstructured data and the carbon trading price to obtain different IMFs, which are reconstructed by the Fine-to-Coarse technique to get low, high frequency sequence and trend sequence. Furthermore, the three items are predicted respectively by using ARIMA, PLS and neural networks according to the features of each scale in time series. Finally, the forecasting results are summed to get the carbon price forecast sequence. The proposed method is used to forecast carbon price in EU. The empirical results show that the prediction accuracy of the model is higher than that of the single prediction method and the prediction method that time series aren’t decomposed by EMD, which is of great applicability.     

大数据时代国防动员潜力调查之路

如何应用大数据提升国防动员潜力调查的效率是当前政府重点关注的话题。从国防动员潜力大数据的定义和特征着手,分析大数据下国防动员潜力调查所具备的优势,发现仍然存在思维局限、指标不统一、体制不健全、技术缺乏及数据共享与防护难协同等困境。通过思维意识、管理体制、指标体系、核心技术、专业人才、安全共享等六大模块,打破“数据不可用、数据不会用”的现实瓶颈,实现整合国防动员数据资源、延展数据内容和提高工作效率的目标。     

大学化学实验课程中有效数字的记录

有效数字是大学化学实验课程的主要教学内容之一,是培养学生在课程中理解并在日常学习与工作中规范使用有效数字,提升其对有效数字与测定误差之间关系的认识水平,树立良好规范的数字记录习惯,具有十分重要的现实意义。论述介绍了目前大学化学实验教学中关于有效数字方面存在的主要问题,并采用相应的举措建立和完善了教学方案以利于更好地解决存在的问题。通过几个学期的教学实践发现新教学方案取得了初步的成效,同时严格按照有效数字的要求记录与计算数据也是培养学生具有严谨科学素养的一条重要途径。     

基于司法大数据的生态环境犯罪刑法惩治分析

生态文明建设是我国社会主义现代化建设的重要组成部分。依法惩治生态环境犯罪是保障生态文明建设的重要手段。文章基于司法大数据对我国刑法规定的生态环境犯罪的发案情况、基本样态、核心成因与治理之策等进行了统计与分析。研究表明,运用刑法手段惩治生态环境犯罪在我国取得了初步效能,尽管污染环境罪等典型生态环境犯罪的发案率近年来处于冲高回落阶段;但由于仍处于工业化进程中,人民群众的生产、生活与生态要素的交互在空间与密度上依然广泛与密集;基于刑法惩治所压制的生态环境犯罪发案与犯罪的原始动能之间仍呈现出一定的二元离散;部分生态环境犯罪发案显示出犯罪人基于生活利益而获取自然资源的客观情形在一定时空范围内依然存在;提升刑法治理生态环境犯罪的效能尚需多方参与、多元共治、多维提升,从而努力实现人民群众生产、生活质量与生态文明建设的同步高质量发展。就微观层面而言,基于司法大数据的统计分析表明,由于新冠疫情防控政策等公共政策的影响,自2020年以来我国生态环境犯罪的发案数量呈现出巨大下降。但需明确的是,此种下降具有强烈的外部性与暂时性,在本质上是由于人民群众基于居家不出而形成的。随着新冠疫情防控政策的常态化、精准化,...     

数字经济的地方法治试验:理论阐释与实践路径

数字经济的异军突起,给作为治理体系和治理能力重要依托的法治秩序带来了挑战,也产生了全新的法治需求。面对新出现的“治理赤字”,如何提供优质的“法治产品”,构建有助于数字经济发展与治理的法治秩序,成为当务之急。但数字经济作为一种新生事物,高度依赖科技创新,处于不断创新、发展与变化之中,具有创新性、跨界性、虚拟性以及平台性等典型特征,由中央层面进行统一的法治供给面临诸多现实困难,也无法有效激活地方数字经济创新与发展的动力。为此,中央通过《国家数字经济创新发展试验区实施方案》,选择在部分地方进行数字经济创新发展与治理的试验,鼓励试验区加强体制机制创新和地方性法规探索。此背景下,数字经济领域的法治建设,除了由中央统一推动诸如《个人信息保护法》《数据安全法》等基础性法律制度的供给外,地方政府以法治试验的方式接入部分法治供给在理论层面何以可能,在实践层面是否具有现实基础、面临那些挑战以及如何克服,便成为不得不重视的问题。基于此,文章借助逻辑推理和实证分析方法,对数字经济领域地方法治试验的理论基础和实践路径进行了探索。研究发现,数字经济的地方法治试验,是一种有效回应数字经济法治需求的经验性治理方式。其...