基于数据挖掘的保健药品关联性分析

信息技术的迅速发展以及数据库技术的日新月异,大量的信息成为当代的一个特点.保健药品企业存储了海量的交易数据,通常,面对这样的数据,人们往往无所适从,数据挖掘的方法向我们提供了对海量数据进行加工和处理的工具,基于企业交易海量数据,建立商品层面关联分析模型,并分别对订购商不加时间区分的进行商品关联分析、按月合并订购商订单后进行商品关联分析,此外,还进一步地进行了商品的序列关联分析.     

函数型数据回归分析综述

随着计算机储存能力和在线观测技术的提高,当今数据越来越多的以曲线和图像的形式存在.曲线和图像数据两个最显著的特征是高维和相邻数据间高度相关.这些特征使得传统的多元统计分析方法不再适合,而函数型数据在处理曲线和图像数据中具有无可比拟的优势.近年来各种各样的函数型数据分析方法得以发展,其中包括数据的对齐、主成分分析、回归、分类、聚类等.本文主要介绍函数型数据回归分析研究的起源、发展及最新进展.具体地,本文首先介绍函数型数据的概念;其次介绍函数型主成分分析方法;再次着重介绍函数型回归模型的估计、变量选择和检验方法;最后将简要探讨函数型数据未来的可能发展方向.     

CFO的大数据时代

在大数据时代．无论是管理会计战略预算,还是流程梳理、成本控制．CFO都必须考虑量级远远超过传统数据库的非结构化数据,他们发现太多改变：集群特征比样本分析重要．相关关系比因果关系重要,未来趋势比具体问题重要。这让那些习惯每一个数字都准确到小数点的传统财务高管们产生了很多迷茫与不适。     

投稿须知

<正>《数理统计与管理》是由中国现场统计研究会主办,公开发行的学术刊物,双月刊,是中国中文核心期刊,也是中国人文核心期刊。本刊主要刊登统计科学、管理科学(限与概率统计方法有关部分)及相关领域的有创造性的理论与方法的研究论文、有较大价值的应用成果、综合评论及动态报道。主要读者对象是统计学专业、管理科学专业和应用数学专业的高等院校师生、企事业单位中的工程技术人员及管理工作者。本刊是"中国学术期刊(光盘版)"的第一批成员。被"中国学术期刊文摘(中、英文)"首批收录为源期刊;也是"中国学术期刊综合评价数据库(CAJCED)"、"中文社会科学引文索引(CSSCI)"等的统计来源期刊。发表的文章被"中国科学引文数据库(CSCD)"、"中国期刊全文数据库(CJFD)"检索收录。     

投稿须知

<正>《数理统计与管理》是由中国现场统计研究会主办,公开发行的学术刊物,双月刊,是中国中文核心期刊,也是中国人文核心期刊。本刊主要刊登统计科学、管理科学(限与概率统计方法有关部分)及相关领域的有创造性的理论与方法的研究论文、有较大价值的应用成果、综合评论及动态报道。主要读者对象是统计学专业、管理科学专业和应用数学专业的高等院校师生、企事业单位中的工程技术人员及管理工作者。本刊是"中国学术期刊(光盘版)"的第一批成员。被"中国学术期刊文摘(中、英文)"首批收录为源期刊;也是"中国学术期刊综合评价数据库(CAJCED)"、"中文社会科学引文索引(CSSCI)"等的统计来源期刊。发表的文章被"中国科学引文数据库(CSCD)"、"中国期刊全文数据库(CJFD)"检索收录。     

投稿须知

<正>《数理统计与管理》是由中国现场统计研究会主办,公开发行的学术刊物,双月刊,是中国中文核心期刊,也是中国人文核心期刊。本刊主要刊登统计科学、管理科学(限与概率统计方法有关部分)及相关领域的有创造性的理论与方法的研究论文、有较大价值的应用成果、综合评论及动态报道。主要读者对象是统计学专业、管理科学专业和应用数学专业的高等院校师生、企事业单位中的工程技术人员及管理工作者。本刊是"中国学术期刊(光盘版)"的第一批成员。被"中国学术期刊文摘(中、英文)"首批收录为源期刊;也是"中国学术期刊综合评价数据库(CAJCED)"、"中文社会科学引文索引(CSSCI)"等的统计来源期刊。发表的文章被"中国科学引文数据库(CSCD)"、"中国期刊全文数据库(CJFD)"检索收录。     

投稿者须知

<正>《数理统计与管理》是由中国现场统计研究会主办,公开发行的学术刊物,双月刊,是中国中文核心期刊,也是中国人文社会科学核心期刊。本刊主要刊登统计科学、管理科学(限与概率统计方法有关部分)及相关领域的有创造性的理论与方法的研究论文、有较大价值的应用成果、综合评论及动态报道。主要读者对象是统计学专业、管理科学专业和应用数学专业的高等院校师生、企事业单位中的工程技术人员及管理工作者。本刊是"中国学术期刊(光盘版)"的第一批成员。被"中国学术期刊文摘(中、英文)"首批收录为源期刊;也是"中国学术期刊综合评价数据库(CAJCED)"、"中文社会科学引文索引(CSSCI)"等的统计来源期刊。发表的文章被"中国科学引文数据库(CSCD)"、"中国期刊全文数据库(CJFD)"检索收录。     

投稿者须知

<正>《数理统计与管理》是由中国现场统计研究会主办,公开发行的学术刊物,双月刊,是中国中文核心期刊,也是中国人文社会科学核心期刊。本刊主要刊登统计科学、管理科学(限与概率统计方法有关部分)及相关领域的有创造性的理论与方法的研究论文、有较大价值的应用成果、综合评论及动态报道。主要读者对象是统计学专业、管理科学专业和应用数学专业的高等院校师生、企事业单位中的工程技术人员及管理工作者。本刊是"中国学术期刊(光盘版)"的第一批成员。被"中国学术期刊文摘(中、英文)"首批收录为源期刊;也是"中国学术期刊综合评价数据库(CAJCED)"、"中文社会科学引文索引(CSSCI)"等的统计来源期刊。发表的文章被"中国科学引文数据库(CSCD)"、"中国期刊全文数据库(CJFD)"检索收录。     

基于人脸重要特征和SVM的人脸识别方法

提出了一种基于人脸重要特征的人脸识别方法,首先选取人脸的重要特征并将其具体化,对得到的重要特征进行主成分分析,然后用支持向量机(Support Vector Machine,SVM)设计重要特征分类器来确定测试人脸图像中重要特征,同时设计支持向量机(SVM)人脸分类器,确定人脸图像的所属类别.对ORL人脸图像数据库进行仿真实验,结果表明,该方法要优于一般的基于整体特征的人脸识别方法并有较强的鲁棒性.     

基于ICA的时间序列聚类方法及其股票数据分析中的应用

时间序列聚类分析是时间序列数据挖掘中的重要任务之一,通常由于时间序列数据的特殊结构,导致一般的聚类算法不能直接应用于时间序列数据.本文提出了一种基于独立成分分析与改进K-均值算法相结合的时间序列聚类算法,该算法首先利用独立成分分析对时间序列数据进行特征提取,然后利用改进K-均值聚类算法完成对时间序列特征数据的聚类分析,从而得到了一种新的基于特征的时间序列聚类方法.为了验证该方法的有效性和可行性,将其应用于实际的股票时间序列数据聚类分析中,取得了较好的数值结果.     

Allfinanz,bancassurance and the future of banking

The purpose with this paper is first to analyse the strategies of Allfinanz (Bancassurance) in Germany and then to investigate if the German banks have become more efficient during the period since Allfinanz was introduced. Two methods are used to analyse efficiency: ‘financial ratio analysis’ (FRA) and ‘data envelopment analysis’ (DEA). FRA is useful in studying the change in productivity. DEA generates, efficiency scores and alternative and more efficient combinations of banks.     

1996~2017年《Operations Research》期刊发文统计分析

《Operations Research》(OR)是运筹与管理科学领域的顶级期刊,对其刊文进行统计分析,剖析国际运筹学科领域的主流研究机构及合作影响,探究当前研究热点问题及学科发展趋势,对于促进我国运筹学科领域的发展具有十分重要的指引作用.基于对WOS核心数据库中该期刊1996至2017年间全部刊文的统计分析,得出以下结论:近年来我国学者发文量稳步提升,表明我国运筹学科领域研究水平的显著提高,但其影响力仍有待进一步提升;机构合作、国际合作都有助于文章引文量的提升;引证分析显示柔性制造(流程柔性及效率)、巢式Logit模型的应用、网络收益管理等7个研究方向是当前、并继续成为运筹领域的研究热点.     

Ethnomathematics: Why, and what else?

Ethnomathematics—we all have some notions of what it is, but should it be influencing school mathematics? This paper considers a justification for the influence from a constructivist perspective, and considers the implications of enactivism and other ways of thinking on ethnomathematics. My conclusion is that the influence should be broader—that different cultural groups may have different ways of knowing, that we may be asking the wrong questions, and that we might need to consider ‘ethno-education’     

An exploratory study on student understandings of derivatives in real-world,non-kinematics contexts

Much research on calculus students’ understanding of applied derivatives has been done in kinematics-based contexts (i.e. position, velocity, acceleration). However, given the wide range of applications in science and engineering that are not based on kinematics, nor even explicitly on time, it is important to know how students understand applied derivatives in non-kinematics contexts. In this study, interviews with six students and surveys with 38 students were used to explore students’ “ways of understanding” and “ways of thinking” regarding applied, non-kinematics derivatives. In particular, six categories of ways of understanding emerged from the data as having been shared by a substantial portion of the students in this study: (1) covariation, (2) invoking time, (3) other symbols as constants, (4) other symbols as implicit functions, (5) implicit differentiation, and (6) output values as amounts instead of rates of change.     

教学测评数据的聚类分析

教学测评是了解教学信息的重要渠道和实施教学管理的重要手段,如何挖掘期资料信息,对了解学生的教学反映,反映教学过程和教学效果,促进教学质量的提高十分重要.本文借助SPSS软件,运用聚类分析等方法,对测评数据进行多元分析,为进一步改进教学测评提供参考.     

Scalar and vector line integrals: A conceptual analysis and an initial investigation of student understanding

This paper adds to the growing body of research happening in multivariable calculus by examining scalar and vector line integrals. This paper contributes in two ways. First, this paper provides a conceptual analysis for both types of line integrals in terms of how theoretical ways of thinking about definite integrals summarized from the research literature might be applied to understanding line integrals specifically. Second, this paper provides an initial investigation of students’ understandings of line integral expressions, and connects these understanding to the theoretical ways of thinking drawn from the literature. One key finding from the empirical part is that several students appeared to understand individual pieces of the integral expression based on one way of thinking, such as adding up pieces or anti-derivatives, while trying to understand the overall integral expression through a different way of thinking, such as area under a curve.     

An ellipsoidal frontier model: Allocating input via parametric DEA

This paper presents the ellipsoidal frontier model (EFM), a parametric data envelopment analysis (DEA) model for input allocation. EFM addresses the problem of distributing a single total fixed input by assuming the existence of a predefined locus of points that characterizes the DEA frontier. Numeric examples included in the paper show EFM’s capacity to allocate shares of the total fixed input to each DMU so that they will all become efficient. By varying the eccentricities, input distribution can be performed in infinite ways, gaining control over DEA weights assigned to the variables in the model. We also show that EFM assures strong efficiency and behaves coherently within the context of sensitivity analysis, two properties that are not observed in other models found in the technical literature.     

Data envelopment analysis application in sustainability: The origins,development and future directions

Sustainable development and sustainability assessment have been of great interest to both academe and practitioners in the past decades. In this study, we review the literature on data envelopment analysis (DEA) applications in sustainability using citation-based approaches. A directional network is constructed based on citation relationships among DEA papers published in journals indexed by the Web of Science database from 1996 to March 2016. We first draw the citation chronological graph to present a complete picture of literature development trajectory since 1996. Then we identify the local main DEA development paths in sustainability research by assigning an importance index, namely search path count (SPC), to each link in the citation network. The local main path suggests that the current key route of DEA applications in sustainability focus on the environmental sustainability. Through the Kamada–Kawai layout algorithm, we find four research clusters in the literature including corporate sustainability assessment, regional sustainability assessment, sustainability composite indicator construction, and sustainability performance analysis. For each of the clusters, we further identify the key articles based on citation network and local citation scores, demonstrate the developmental trajectory of the literature, and suggest future research directions.     

Identifying indicators of chemical,biological, radiological,and nuclear (CBRN) weapons development activity in sub-national terrorist groups

Data collected on known terrorist organizations allow intelligence agencies to build a statistical database of features for each group and an observed level of development of chemical, biological, radiological, or nuclear (CBRN) weapons. For the intelligence analyst, a statistical exploration of the structure of the multivariate data is helpful for determining which subset of features—and the relative contribution of each feature in the subset—best discriminate between levels of CBRN weapons development. The resulting function that is used to discriminate between CBRN development levels is called the ‘classifier’. Once the appropriate subset of indicators has been identified and a classifier developed, intelligence agencies will be better able to focus their information gathering and to assess the effect that changes in a terrorist group's features will have on their CBRN weapons development. Additionally, the classifier will enable the intelligence agency to predict the CBRN weapons development level of terrorist group where the feature set of the group is known but the level is unknown. In this analysis, we compare three approaches for building a classifier that best predicts CBRN weapons development levels using a training set with 45 observations; (1) heuristic pattern recognition approach that couples a weighted Minkowski distance metric with a nonparametric kernel-based classification method, (2) classification trees, and (3) discriminant analysis. Where possible, cross-validation is conducted on the data to ensure that the resulting classifier is not overly dependent on the training set. This initial analysis provides some interesting results and suggests a reasonable starting point for finding structure in the data as more observations are added.