VisIOT:经济产业结构关联可视分析

随着经济统计范畴的精细化以及统计过程的规范化，能够表现产业部门关联关系的投入产出表（Input-Output Table，IOT）数据日益呈现复杂的结构特性。传统的统计分析软件和方法形式单一且传达信息有限，面对结构关系复杂且动态演化的IOT数据，难以有效分析和探索其中复杂的关联模式和时序变化特征。为此，本文设计面对IOT数据分析的经济产业结构关联特征可视化工具——VisIOT。首先设计双向力导向图描述国民经济结构关联网络，并对网络中的顶点和边进行属性映射；然后构建时序矩阵图，直观地展示IOT数据差异，并按照时间顺序依次嵌入时序IOT数据；其次利用部门间的经济技术联系优化模块度算法，发掘经济产业结构关联网络中隐含的社区特征，有效支持关联紧密的社区结构的交互式分析和提取；再次设计社区时序演变图展示社区结构特征的时序演化规律，借助交叉优化算法和前后向的扫描算法，优化部门排列顺序，减少部门交叉，帮助用户有效捕捉社区结构的稳定性；最后有效设计交互方案关联可视化界面，实现经济产业结构关联可视分析系统。本文利用真实的IOT数据进行实例分析与验证，结果表明本文设计的VisIOT系统能够帮助用户快速识别和感知IOT数据中隐含的关联特征及其时序变化规律。     

信息时代如何提高统计人员职业技能初探

一、信息时代统计工作的特征 （一）统计信息网络系统正在成为支撑统计工作运行的基础和技术保障系统。经过多年努力，目前，统计信息化建设已经形成上下贯通、安全可靠的网络环境，技术装备。从网络的覆盖情况来看，全国基本上所有的地市和副省级城市都完成了统计信息网络接人工程，一些地方还开通了视频系统；三分之一以上的县已经连通了网络。国家统计局机关的服务器实行了集中托管制度；国家统计信息主干网的64个网络节点也实行了统一的动态管理。同时，在网络技术的支持下，统计信息内外网、OA系统也得到较快发展。     

基于变系数模型的高维数据异同性识别方法研究

随着信息技术的发展,高维数据日益丰富。现实中,很多高维数据由多个主体各异的数据集融合而成。如何准确识别出高维数据集间的异同性成为大数据分析的目标之一。本文提出了变系数模型下的高维数据整合分析方法。该方法可以同时对多个数据集进行变量选择和系数估计,并且能 够自动识别出变量系数在数据集间的异同性。模拟结果表明本文方法在异同性识别、变量选择、系数估 计和预测等方面明显优于对比方法。在肺癌致病基因识别的应用研究中,本文方法能够识别出具有生物解释的致病基因并发现了两种亚型之间的异同性。     

面向有向赋权网络的节点重要性度量方法研究

众多现实问题可以建模为有向赋权网络中节点重要性的度量问题。文章从节点不同连接方式的角度出发,区分网络中节点的直接连接、桥梁连接以及间接连接方式对有向赋权网络的损失,提出了面向有向赋权网络的节点相对重要性的度量方法;通过对比节点重要性不同度量方法,说明该方法能更细致地凸显节点之间的差异性,并比较客观地反映节点的物理属性以及节点的网络结构位置对有向赋权网络整体的影响作用。     

基于贝叶斯网络和GPS轨迹数据的出行方式识别

为了从大规模GPS轨迹数据中提取出行行为建模所需的必要信息,文章将贝叶斯网络应用到GPS数据处理过程中,建立了出行方式识别的贝叶斯网络模型.以个体出行者作为研究对象,以智能手机采集的轨迹信息作为数据源,利用K2算法学习贝叶斯网络结构,采用极大似然估计法学习贝叶斯网络参数.以建立的贝叶斯网络模型为基础,推断了样本的出行方式,实现了步行、自行车、电动车、公交车和小汽车共五种出行方式的自动化识别.研究表明,贝叶斯网络适用于出行方式识别研究,且低速点比例和平均方向改变两个指标可以有效提高出行方式识别准确度.     

偶发因素对非正式创新网络影响的实证研究

研究偶发因素对非正式创新网络IIN的节点属性和网络结构特性的影响。研究发现:外生偶发因素会正向影响非正式创新网络IIN的网络节点静态属性,导致改变网络节点的行为,从而对网络节点动态属性和网络结构静态属性产生显著的正向影响,特别是网络外生偶发因素会正向影响非正式创新网络IIN的网络接入意愿和接入频率。     

贝叶斯方法在证券市场中的应用

证券市场中股票的统效对投资者而言显得特别重要，股票绩效总是从以往股票数据的统计分析获得的。投资者试图在小的投资区域获得绩优胜，这就有赖于对数据进行进一步的分析。贝叶斯统计方法是以新获得的数据信息更新先验信息而对数据进行推断，本文是将贝叶斯方法运用于证券市场的一点粗浅尝试。一、公式及推断已知H表示发生了某件事件，H表示某件事件没有发生，E表示已知某一信息，E表示未知某一信息。相应地，P（H）表示H的先验概率，P（HIE）表示H的后验概率，P（HE）表示H、E的联合概率。我们可得到贝叶斯公式：用0（H）表示H的…     

中国数字创意产业技术创新空间关联网络演变特征及驱动因素研究

文章运用社会网络分析方法和修正的引力模型分析了数字创意产业技术创新的空间关联网络演变特征，并借助QAP方法识别了其驱动因素。结果表明：（1）我国数字创意产业技术创新空间关联表现为多流向、多线程的典型网络结构形态，但整体网络结构有待进一步优化。（2）各省份在空间关联网络中的点度中心度、中介中心度和接近中心度数值整体表现为上升趋势，北京等8个省份在网络中发挥着“发动机”的作用，江苏等7个省份在网络中发挥着“桥梁”和“中介”的作用，上海等14个省份在网络中扮演着“中心行动者”角色。（3）各板块间的空间联系紧密程度由较为疏松趋于较为紧密，协同、联动效应有较大的提升，但同样存在着非均衡、逆向溢出的问题。（4）地理位置相邻、信息化水平差异、基础设施水平差异、市场开放程度差异、政府创新支持力度差异对空间关联有着显著的正向影响，人力资本状况差异对空间关联有着显著的负向影响。     

移动社交网络的知识溢出效应——信息红利还是数字鸿沟？

知识溢出是提升人力资本进而促进经济增长的重要方式，移动社交网络的出现和广泛使用打破了信息传递的时空限制，但目前关于其是否具有知识溢出效应的研究仍十分缺乏。本文使用三次中国家庭金融调查（CHFS）采集的具有全国代表性的微观家庭和社区面板数据，以金融知识为研究对象，利用社区是否创建微信群作为准实验，采用双重差分（DID）与三重差分模型（DDD）实证考察微信群这一移动社交网络是否具有知识溢出效应。研究发现，社区建立微信群可使群内成员的金融知识水平显著提升约17.5%，且这一效应在农村地区、受教育程度较低、年龄较大的群体中更为显著，这表明移动社交网络可通过知识溢出创造信息红利并缩小数字鸿沟。进一步的机制分析发现，移动社交网络既可通过其信息源渠道激励群成员学习，也可通过交流渠道促进群成员在互动交流中提高知识水平。本文研究结论表明，在移动社交网络广泛普及的背景下，充分利用网络空间思想集聚产生的知识外溢可以为经济发展创造新动能。     

社会网络分析视角下复杂网络结构关系的综合测度

为了克服复杂网络节点数量繁多、结构关系不易测度的难题,文章提出一种社会网络分析视角下“以点界面”的研究思路.通过构建一个新的综合测度指标,完成对网络中节点的综合评价,实现对网络结构关系的测度.与现有社会网络单一测度指标相比,综合测度指标突破现有单一测度指标的局限,构建过程充分利用单一测度指标间关系规律.复杂网络仿真实验进一步表明,无论有中心结构的网络结构和无中心结构的网络结构,综合测度指标提高网络节点不同评价维度和网络角色自带信息的利用率,对网络中节点的全面刻画更为贴切,为复杂网络结构关系综合测度研究提供一种新工具,为提高生活生产效率、预防灾害负面影响提供参考和依据.     

A metadata approach to statistical query processing

Concerning the task of integrating census and survey data from different sources as it is carried out by supranational statistical agencies, a formal metadata approach is investigated which supports data integration and table processing simultaneously. To this end, a metadata model is devised such that statistical query processing is accomplished by means of symbolic reasoning on machine-readable, operative metadata. As in databases, statistical queries are stated as formal expressions specifying declaratively what the intended output is; the operations necessary to retrieve appropriate available source data and to aggregate source data into the requested macrodata are derived mechanically. Using simple mathematics, this paper focuses particularly on the metadata model devised to harmonize semantically related data sources as well as the table model providing the principal data structure of the proposed system. Only an outline of the general design of a statistical information system based on the proposed metadata model is given and the state of development is summarized briefly.     

大数据背景下基于社交网络的聚类随机游走抽样算法研究

数字经济时代,社交网络作为数字化平台经济的重要载体,受到了国内外学者的广泛关注。大数据背景下,社交网络的商业应用价值巨大,但由于其网络规模空前庞大,传统的网络分析方法 因计算成本过高而不再适用。而通过网络抽样算法获取样本网络,再推断整体网络,可节约计算资源, 因此抽样算法的好坏将直接影响社交网络分析结论的准确性。现有社交网络抽样算法存在忽略网络内部拓扑结构、容易陷入局部网络、抽样效率过低等缺陷。为了弥补现有社交网络抽样算法的缺陷,本文结合大数据社交网络的社区特征,提出了一种聚类随机游走抽样算法。该方法首先使用社区聚类算法将原始网络节点进行社区划分,得到多个社区网络,然后分别对每个社区进行随机游走抽样获取样本网 络。数值模拟和案例应用的结果均表明,聚类随机游走抽样算法克服了传统网络抽样算法的缺点,能够在降低网络规模的同时较好地保留原始网络的结构特征。此外,该抽样算法还可以并行运算,有效提升抽样效率,对于大数据背景下大规模社交网络的抽样实践具有重大现实意义。     

Statistical studies of infectious disease incidence

Methods for the analysis of data on the incidence of an infectious disease are reviewed, with an emphasis on important objectives that such analyses should address and identifying areas where further work is required. Recent statistical work has adapted methods for constructing estimating functions from martingale theory, methods of data augmentation and methods developed for studying the human immunodeficiency virus–acquired immune deficiency syndrome epidemic. Infectious disease data seem particularly suited to analysis by Markov chain Monte Carlo methods. Epidemic modellers have recently made substantial progress in allowing for community structure and heterogeneity among individuals when studying the requirements for preventing major epidemics. This has stimulated interest in making statistical inferences about crucial parameters from infectious disease data for such community settings.     

Statistical Modelling of Brain Morphological Measures Within Family Pedigrees

Large, family-based imaging studies can provide a better understanding of the interactions of environmental and genetic influences on brain structure and function. The interpretation of imaging data from large family studies, however, has been hindered by the paucity of well-developed statistical tools for that permit the analysis of complex imaging data together with behavioral and clinical data. In this paper, we propose to use two methods for these analyses. First, a variance components model along with score statistics is used to test linear hypotheses of unknown parameters, such as the associations of brain measures (e.g., cortical and subcortical surfaces) with their potential genetic determinants. Second, we develop a test procedure based on a resampling method to assess simultaneously the statistical significance of linear hypotheses across the entire brain. The value of these methods lies in their computational simplicity and in their applicability to a wide range of imaging data. Simulation studies show that our test procedure can accurately control the family-wise error rate. We apply our methods to the detection of statistical significance of gender-by-age interactions and of the effects of genetic variation on the thickness of the cerebral cortex in a family study of major depressive disorder.     

Locating genes involved in human diseases

The increasing amount of information that is becoming available about the structure and composition of the DNA constituting the human chromosomes has provided new opportunities to locate genes that affect susceptibilities to a range of diseases. The accurate location of these genes is important in genetic counselling and in understanding the effects of genes that may result in disease. Various methods of analysing the data when DNA information is available at a single marker locus for an affected child and his or her parents are reviewed and applied to data on insulin-dependent diabetes mellitus . The importance of distinguishing between the association of alleles at a marker locus and at a disease locus resulting from chromosomal linkage from that resulting from other causes is emphasized.     

Community detection with structural and attribute similarities

An important problem in network analysis is to identify significant communities. Most of the real-world data sets exhibit a certain topological structure between nodes and the attributes describing them. In this paper, we propose a new community detection criterion considering both structural similarities and attribute similarities. The clustering method integrates the cost of clustering node attributes with the cost of clustering the structural information via the normalized modularity. We show that the joint clustering problem can be formulated as a spectral relaxation problem. The proposed algorithm is capable of learning the degree of contributions of individual node attributes. A number of numerical studies involving simulated and real data sets demonstrate the effectiveness of the proposed method.     

USING LINEAR PREDICTORS TO IMPUTE ALLELE FREQUENCIES FROM SUMMARY OR POOLED GENOTYPE DATA

Recently-developed genotype imputation methods are a powerful tool for detecting untyped genetic variants that affect disease susceptibility in genetic association studies. However, existing imputation methods require individual-level genotype data, whereas in practice it is often the case that only summary data are available. For example this may occur because, for reasons of privacy or politics, only summary data are made available to the research community at large; or because only summary data are collected, as in DNA pooling experiments. In this article, we introduce a new statistical method that can accurately infer the frequencies of untyped genetic variants in these settings, and indeed substantially improve frequency estimates at typed variants in pooling experiments where observations are noisy. Our approach, which predicts each allele frequency using a linear combination of observed frequencies, is statistically straight-forward, and related to a long history of the use of linear methods for estimating missing values (e.g. Kriging). The main statistical novelty is our approach to regularizing the covariance matrix estimates, and the resulting linear predictors, which is based on methods from population genetics. We find that, besides being both fast and flexible - allowing new problems to be tackled that cannot be handled by existing imputation approaches purpose-built for the genetic context - these linear methods are also very accurate. Indeed, imputation accuracy using this approach is similar to that obtained by state-of-the art imputation methods that use individual-level data, but at a fraction of the computational cost.     

A review and discussion of prospective statistical surveillance in public health

Summary. A review of methods suggested in the literature for sequential detection of changes in public health surveillance data is presented. Many researchers have noted the need for prospective methods. In recent years there has been an increased interest in both the statistical and the epidemiological literature concerning this type of problem. However, most of the vast literature in public health monitoring deals with retrospective methods, especially spatial methods. Evaluations with respect to the statistical properties of interest for prospective surveillance are rare. The special aspects of prospective statistical surveillance and different ways of evaluating such methods are described. Attention is given to methods that include only the time domain as well as methods for detection where observations have a spatial structure. In the case of surveillance of a change in a Poisson process the likelihood ratio method and the Shiryaev–Roberts method are derived.