2008年新批准的14门数学与统计学精品课程

数学类：概率论（北京大学,陈大岳）,高等数学（北京航空航天大学,郑志明）,数值分析（大连理工大学,于波）,高等数学（合肥工业大学,朱士信）,复变函数（湖州师范学院,刘太顺）,泛函分析（内蒙古大学,孙炯）,代数与几何（清华大学,张贺春）,数学分析与习题课（苏州大学,谢惠民）,金融衍生物定价理论（同济大学,姜礼尚）,数学建模（厦门大学,谭忠）,高等数学（郑州大学,李梦如）,数值分析（中南大学,韩旭里）．     

独立成分分析与主成分分析在股票市场上的运用

展示了分析及认识金融市场结构的一种新方法.将这种方法实例应用在了股票指数CAC40的一篮子股票中,对股票价格走势进行分析,并将独立成分分析（ICA）方法与传统的主成分分析（PCA）方法进行了比较.     

一类Marcinkiewicz积分交换子的Hardy空间估计

In this paper, we obtain the （H^1,L^n/（n-β） and （HKq1^n（1-1/q2）,p,Kq2^n（1-1/q1）,p） type estimates for the commutator of Marcinkiewicz integral with the kernel satisfying the logarithmic type Lipschitz conditions.     

UMD空间及其应用

UMD空间是被广泛研究的一类新型的Banach空间，它具有一系列良好的几何性质与分析性质并且与向量值调和分析、随机分析有着广泛深刻的联系。本扼要介绍这类空间的有关问题，主要是以下几个方面：（1）引言（定义与产生背景）；（2）UMD空间的几何特性与分析特征；（3）此类空间的例；（4）在向量值调和分析理论中的应用；（5）关于鞅不等式的最优系数问题。     

广义函数δ（x_1,…,x_（2m））和δ（x_1,…,x_（2m-1））I（x_（2m））的乘积（英文）

AB 用非标准分析和广义函数的调和表示,给出广义函数δ（x_1,…,x_（2m））和δ（x_1,…,x_（2m-1））Ⅰ（x_（2m））的乘积.     

统计在生命科学中的实践与认识(V)Meta-分析(下)

统计在生命科学中的实践与认识（Ｖ）Ｍｅｔａ－分析（下）汤旦林，李晓强（中日友好医院１０００２９）五、综合不同研究结果的分析方法Ｍｅｔａ－分析的基本方法是，将收集到的统计量或效应指标进行适当的综合；分析效应变异的来源。主要的分析过程包括：统计量的（加权...     

ANOM和ANOR及其应用

１．问题的提出某电池厂为了提高二氧化锰的利用率，了解正极合剂配方对电池性能的影响，需要对Ｒ_６型干电池正极合剂配方进行优选试验，寻找出间隙放电时间长而稳定的配方．选用Ｌ_９（３~４）正交表做工艺参数重复优选试验；并用均值分析（ＡＮＯＭ）和极差分析（ＡＮＯＲ）对试验结果进行了分析，找出了Ｒ６型干电池正极合剂的优选且稳定的配方．具体设计为：我们对锰粉（Ａ）、碳（Ｂ）、氯化氨（Ａ）、水（Ｄ）等四个因素进行优选．将各因素分别取三个水平见表１．把Ａ、Ｂ．Ｃ、Ｄ四个因素顺次放入正交表Ｌ_９（３~４），且不考虑因…     

复Clifford分析中一个方程组的几个性质

本文引进一个组（Mn）,研究复Clifford分析中与它相关联的一种双曲调和函数,得到组（Mn）的几个性质及一个可解充要条件。     

一类小波的正则阶估计

１引言我们知道,一个可生成Ｌ２（Ｒ）中的多分辩分析的尺度函数（ｘ）满足双尺度差分方程（１）式的Ｆｏｕｒｉｅｒ变换为其中（ω）是函数（ｘ）的Ｆｏｕｒｉｅｒ变换,而叫做小波的共轭滤波器（简称滤波器）,它满足若将滤波器Ｈ（ω）设成则条件（３）成为可形式地写出Ｄａｕｂｅｃｈｉｅｓ［１］给出了（６）式无穷乘积收敛的条件．她还针对Ｆ（ｚ）为多项式情形（此时称滤波器为多项式滤波器）,给出了产生紧支小波的方法并给出了正则阶估计［２］．在［３］中,作者给出了小波分式滤波器的定义（即（４）式中Ｆ（ｚ））是实系数有理…     

EXACT AND DISCRETIZED DISSIPATIVITY OF THE PANTOGRAPH EQUATION

The analytic and discretized dissipativity of nonlinear infinite-delay systems of the form x＇（t） = g（x（t）,x（qt））（q∈ （0, 1）, t 〉 0） is investigated. A sufficient condition is presented to ensure that the above nonlinear system is dissipative. It is proved the backward Euler method inherits the dissipativity of the underlying system. Numerical examples are given to confirm the theoretical results.     

统计方法在研究微积分与后续课程相关性中的应用与实证分析

着眼于本科生后期专业课程的学习效果,探讨微积分课程学习效果的有效性.以北京理工大学某经管类专业全部学生大一大二两学年的学习成绩为依据,分析微积分课程学习与后续理科课程学习的相关性,提出评估微积分课程学习效果的量化指标θ值的概念,并基于回归分析和相关性分析给出其算法.在实例分析中,通过研究,发现学生微积分课程学习效果的好坏会影响他们对后续相关课程的学习,同时也发现学生将微积分知识运用到间接相关科目的能力比运用到直接相关的科目的能力薄弱.     

Enabling e-transactions with multi-attribute preference models

This paper describes potential applications of multi-attribute preference models (MAPM) in e-commerce and offers some guidelines for their implementation. MAPM are methodologies for modeling complex preferences that depend on more than one attribute or criterion, and include multi-attribute utility theory, conjoint analysis, and the Analytic Hierarchy Process. There are numerous examples of applications in e-commerce that would benefit from the acquisition of information regarding the preferences of a consumer, a customer, an advice seeker, or a decision maker. Here, the focus is on applications of MAPM models in B2C and B2B websites, where preferences of consumers are assessed for the purpose of identifying products or services that closely match their needs.     

初中学生心理测量的统计分析

本文用相关分析说明了〈中学生素质特点分类培养〉项目研究中所用各种心理诊断量表整体组合运用的合理性 ;用因子分析简化了测试项目的指标体系 ,选定了适当的公共主因子 ,并对公因子给予了合理的解释 ;用聚类分析依据因子得分对学生实施心理素质特点分类 ;根据学生的心理素质特点类型提出了相应的宏观培养策略     

我国工业污染分布状况研究

环境污染越来越受到我国的重视,而工业污染是造成环境污染的重要原因.为了对我国的环境污染分布状况进行深入了解,从总量出发,利用聚类分析和因子分析法分析了我国工业污染的分布情况,并探讨了各类地区工业污染差异的原因,为我国进行侧重点治理环境污染提出可行性建议.     

Singular Value Decomposition and Its Visualization

Singular value decomposition (SVD) is a useful tool in functional data analysis (FDA). Compared to principal component analysis (PCA), SVD is more fundamental, because SVD simultaneously provides the PCAs in both row and column spaces. We compare SVD and PCA from the FDA view point, and extend the usual SVD to variations by considering different centerings. A generalized scree plot is proposed to select an appropriate centering in practice. Several useful matrix views of the SVD components are introduced to explore different features in data, including SVD surface plots, image plots, curve movies, and rotation movies. These methods visualize both column and row information of a two-way matrix simultaneously, relate the matrix to relevant curves, show local variations, and highlight interactions between columns and rows. Several toy examples are designed to compare the different variations of SVD, and real data examples are used to illustrate the usefulness of the visualization methods.     

Actor analysis methods and their use for public policy analysts

Public policy analysts use methods rooted in OR and systems analysis to support policy makers in their judgement. In doing so, most policy analysts recognize the value of a certain understanding of the role of actors in policy making processes. Different methods are available to aid such understanding and, although they all focus on actors, there are important differences between them. Insight into the range of available methods and their characteristics will thus help policy analysts to learn more about the potential and limitations involved in analyzing multi-actor processes. This article provides such an overview, based on the main requirements these methods should meet. This overview is used to discuss some of the implications for policy analysts who are interested in analyzing multi-actor processes, focusing specifically on trade-offs between analytic quality and practical usability.     

What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions

Evaluating the economic attractiveness of large projects often requires the development of large and complex financial models. Model complexity can prevent management from obtaining crucial information, with the risk of a suboptimal exploitation of the modelling efforts. We propose a methodology based on the so-called “differential importance measure (D)$(D)$” to enhance the managerial insights obtained from financial models. We illustrate our methodology by applying it to a project finance case study. We show that the additivity property of D grants analysts and managers full flexibility in combining parameters into any group and at the desired aggregation level. We analyze investment criteria related to both the investors’s and lenders’ perspectives. Results indicate that exogenous factors affect investors (sponsors and lenders) in different ways, whether exogenous variables are considered individually or by groups.     

A sensitivity analysis algorithm for hierarchical decision models

In this paper, a comprehensive algorithm is developed to analyze the sensitivity of hierarchical decision models (HDM), including the analytic hierarchy process and its variants, to single and multiple changes in the local contribution matrices at any level of the decision hierarchy. The algorithm is applicable to all HDM that use an additive function to derive the overall contribution vector. It is independent of pairwise comparison scales, judgment quantification techniques and group opinion combining methods. The allowable range/region of perturbations, contribution tolerance, operating point sensitivity coefficient, total sensitivity coefficient and the most critical decision element at a certain level are identified in the HDM SA algorithm. An example is given to demonstrate the application of the algorithm and show that HDM SA can reveal information more significant and useful than simply knowing the rank order of the decision alternatives.     

江苏省区域经济发展水平分析

针对江苏各城市间经济发展水平的差异性,本文选用十个指标构建综合评价体系,利用SPSS软件进行因子分析和聚类分析.结果表明13个地区之间经济发展差异较为突出,主要受综合实力因子和农业因子影响.大体分为三类:经济发达城市、经济次发达城市、经济发展滞后城市.最后,文章提出缩小江苏区域经济发展差异的一些建议.     

When does data envelopment analysis outperform a naïve efficiency measurement model?

This paper compares the results from data envelopment analysis (DEA) to a naïve efficiency measurement model, which generates a scalar efficiency score by averaging all output–input ratios. Random data and real-life data are used to test the relative performance of the naïve model against various DEA models. The results suggest that the proposed the naïve model replicates DEA efficiency scores almost perfectly for constant return-to-scales and low heterogeneity in output–input data. It is therefore concluded that heterogeneity in output–input data is important to take advantage of the capability of DEA. It is also shown that heterogeneity is more relevant to efficiency measurement than the number of dimensions.