Recent models in data envelopment analysis: Theory and applications

A critical review of the recent models of data envelopment analysis (DEA) is attempted here. Three new lines of approach involving dynamic changes in parameters, the error correction models and a stochastic sensitivity analysis are discussed in some detail. On the applications side, two new formulations are presented and discussed, e.g. a model of technical change and a cost frontier for testing economies of scale and adjustment due to risk factors. Thus the critical review of recent DEA models of productivity measurement provides new insight into the frontier of research in this field.     

Determining Teaching and Research Efficiencies

In this paper we consider the problem of determining teaching and research efficiencies for university departments concerned with the same discipline. Considering this problem highlights the issue of how to determine efficiencies when resources are shared between different activities, and a non-linear approach to this issue based upon data envelopment analysis is presented. Computational results are given for chemistry and physics departments in the United Kingdom.     

Numerical simulation based on fuzzy stochastic analysis

In this paper mathematical methods for fuzzy stochastic analysis in engineering applications are presented. Fuzzy stochastic analysis maps uncertain input data in the form of fuzzy random variables onto fuzzy random result variables. The operator of the mapping can be any desired deterministic algorithm, e.g. the dynamic analysis of structures. Two different approaches for processing the fuzzy random input data are discussed. For these purposes two types of fuzzy probability distribution functions for describing fuzzy random variables are introduced. On the basis of these two types of fuzzy probability distribution functions two appropriate algorithms for fuzzy stochastic analysis are developed. Both algorithms are demonstrated and compared by way of an example.     

Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data

Among the large amount of genes presented in microarray gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. In this regard, a new feature selection algorithm is presented based on rough set theory. It selects a set of genes from microarray data by maximizing the relevance and significance of the selected genes. A theoretical analysis is presented to justify the use of both relevance and significance criteria for selecting a reduced gene set with high predictive accuracy. The importance of rough set theory for computing both relevance and significance of the genes is also established. The performance of the proposed algorithm, along with a comparison with other related methods, is studied using the predictive accuracy of K-nearest neighbor rule and support vector machine on five cancer and two arthritis microarray data sets. Among seven data sets, the proposed algorithm attains 100% predictive accuracy for three cancer and two arthritis data sets, while the rough set based two existing algorithms attain this accuracy only for one cancer data set.     

变维数自适应神经模糊推理策略及财务诊断应用

提出一种基于主成分分析的变维数策略,用以克服自适应神经模糊推理系统(ANFIS)难以适应数据多维数情况的缺陷。然后结合企业财务分析领域的特点,将优化的自适应神经模糊推理系统应用于公司财务状况诊断。最后,利用样本公司实际指标数据对模型的诊断能力进行了实证研究,结果显示了这种方法的优越性。     

Design,Concepts, and State of the Art of the swMATH Service

In this paper, the concepts and design for an efficient information service for mathematical software and further mathematical research data are presented. The publication-based approach and the web-based approach are the main building blocks of the service and will be discussed. Heuristic methods are used for identification, extraction, and ranking of information about software and other mathematical research data. The methods provide not only information about the research data but also link software and mathematical research data to the scientific context.     

On the performance of universities in North Rhine-Westphalia,Germany: Government’s redistribution of funds judged using DEA efficiency measures

In Fandel and Gal (2001) [Fandel, G., Gal, T., 2001. Redistribution of funds for teaching and research among universities: The case of North Rhine-Westphalia, European Journal of Operational Research 130, 111–120] a solution for a real process of redistributing funds for teaching and research among the universities in North Rhine-Westphalia in Germany according to specific criteria was presented. The solution was based on aggregated proportional data and was determined by a negotiation process. Now that the absolute data with respect to the different subject areas of the universities are available, it is studied, to which extent the results of the redistribution can be justified by the relative efficiency measures using data envelopment analysis. Moreover, the inefficiencies or slacks, respectively, in the usage of personnel give reasons to reallocate the staff among the universities or to reduce it correspondingly.     

An applied statistics course for systematics and ecology PhD students

Statistics education is under review at all educational levels. Statistical concepts, as well as the use of statistical methods and techniques, can be taught in at least two contrasting ways. Specifically, (1) teaching can be theoretically and mathematically oriented, or (2) it can be less mathematically oriented being focused, instead, on application and the use of data to solve real-world problems. The second approach is growing in practice and new goals have recently emerged. At present, statistics courses stress probability concepts, data analysis, and the interpretation and communication of results. Understanding the process of statistical investigation is established as a way of improving mastery of statistical reasoning. In this context, a project-based approach allows the design and implementation of participating learning scenarios in order to understand the statistical methodology and, as a consequence, improve research. This approach points out that statistics is a rational methodology used to solve practical problems. The purpose of this paper is to present the design and results of an applied statistics course for PhD students in ecology and systematics using a project-based approach. Examples involving character coding, species classification, and the interpretation of geographical variation, which are the principal systematic analyses requiring statistical techniques, are presented using the results from student projects. In addition, an example from conservation ecology is presented. Results indicate that the students understood the concepts and applied the systematic and statistical techniques accurately using a data oriented approach.     

基于分位点自回归模型的动态持续期风险估计

两笔交易之间的持续期可以用来度量资产的流动性,本文借鉴VaR的思想,提出了持续期风险DaR的定义,可以用来度量资产的流动性风险,并给出了两种DaR的静态估计方法。同时根据持续期数据的序列相关的特点,应用分位点自回归方法得到了DaR的一种有效的动态估计方法。最后对我国股票市场的单只股票的分笔交易数据进行了实证分析,结果表明由分位点自回归方法得出的DaR结果预测效果最好。     

Sensitivity and stability analysis in fuzzy data envelopment analysis

As a useful management and decision tool, data envelopment analysis (DEA) has become a pop area of research. One of the topics of interests in DEA is sensitivity and stability analysis of decision making units (DMUs). Due to the uncertainty of the data in real life, this paper will give some DEA models in fuzzy environment. It is followed by a series analysis of sensitivity and stability for all DMUs. Finally a numerical example will be presented to give an illustration of the sensitivity and stability analysis.     

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With the advance of computer storage capacity and online observation technique, more and more data are collected with curves and images. The most two important feature of curve and image data are high-dimension and high correlation between adjacent data. Functional data analysis has more advantage in deal with these data, which can not be treated by traditional multivariate statistics methods. Recently, a variety of functional data methods have been developed, including curve alignment, principal component analysis, regression, classification and clustering. In this paper, we mainly introduce the origins,development and recent process of functional data. Specifically, we firstly introduce the notion of functional data. Secondly, functional principal component analysis has been presented. Then, this paper is devoted to introduce estimation, variable selection and hypothesis testing of functional regression models. Lastly, the paper concludes with a brief discussion of future directions.     

A skew–normal mixture of joint location,scale and skewness models

Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this paper, we propose and study a novel class of models: a skew–normal mixture of joint location,scale and skewness models to analyze the heteroscedastic skew–normal data coming from a heterogeneous population. The issues of maximum likelihood estimation are addressed. In particular, an Expectation–Maximization(EM) algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments. Results from the analysis of a real data set from the Body Mass Index(BMI) data are presented.     

Humans-with-media and continuing education for mathematics teachers in online environments

This paper begins by situating online mathematics education in Brazil within the context of research on digital technology over the past 25?years. I argue that Brazilian research on technology in mathematics education can be divided into four phases, and then present an example that ??blends?? aspects of the second and third phases. Phase two can be characterized by research with software designed to address traditional mathematics topics, such as functions, while the third phase is characterized by online courses. The data presented show creative solutions for a problem designed for collectives of humans-with-function-software. The paper is analyzed from a perspective that emphasizes the role of different technologies as teachers and professors collaborate to produce knowledge about the use of mathematical software in regular face-to-face classrooms. A model of online education is presented. Finally, the paper discusses how technology may change collaboration and teaching approaches in continuing education, as it allows for greater integration of online learning with teachers?? classroom activities in schools. In this case, the online platform plays an active role in the learning collective composed of humans-with-media.     

Revenue-based capacity utilisation measures and decomposition: The case of Danish North Sea trawlers

In fisheries, capacity analysis has largely been limited to measuring physical capacity, defined as the maximum amount of output that can be produced per unit of time, given existing plant and equipment and unrestricted availability of variable inputs. An economic measure of capacity can be defined as the maximum revenue attainable for the given fixed inputs, using relevant outputs and output prices. This paper examines these two approaches to capacity by applying data envelopment analysis to physical and economic input/output data for Danish North Sea trawlers. The economic and physical measures are compared and contrasted using correlation analysis. An innovative analysis into the composition of possible revenue increments is also presented and reasons for economic inefficiency of vessels are identified.     

Exponential distance-based fuzzy clustering for interval-valued data

In several real life and research situations data are collected in the form of intervals, the so called interval-valued data. In this paper a fuzzy clustering method to analyse interval-valued data is presented. In particular, we address the problem of interval-valued data corrupted by outliers and noise. In order to cope with the presence of outliers we propose to employ a robust metric based on the exponential distance in the framework of the Fuzzy C-medoids clustering mode, the Fuzzy C-medoids clustering model for interval-valued data with exponential distance. The exponential distance assigns small weights to outliers and larger weights to those points that are more compact in the data set, thus neutralizing the effect of the presence of anomalous interval-valued data. Simulation results pertaining to the behaviour of the proposed approach as well as two empirical applications are provided in order to illustrate the practical usefulness of the proposed method.     

正交平衡区组设计统计分析模型参数估计的分布特征研究

正交平衡区组设计(或者广义正交表)是一种类似于正交拉丁方(或者正交表)的新设计,但试验次数大幅减少.通过对正交平衡区组设计统计分析模型参数估计的分布特征进行了深入研究.研究发现,在试验数据正态性的情况下,各种参数估计也服从正态分布,并且各种参数的最小二乘估计都是无偏的,得到了各种参数估计的方差和独立性性质.     

基于FA-DEA聚类的模式分析方法及在我国软件园研究中的应用

模式分析方法日益成为现代经济管理研究的主流方法之一,但是目前关于模式分析的结构化方法的研究还不多见.本文提出了基于FA-DEA聚类的模式研究结构化方法,这个方法区别于以往文献的是采用了混合聚类方法,综合运用关键因素的绝对量信息和效率的相对量信息.基于上述方法,本文确定了我国软件园发展的三种发展模式.并对三个模式的条件、特征以及它们之间的分界与演进做了详细的描述.最后,给出了若干建议.     

Data analysis and decision support

In this paper, we consider developmental lines of computer-assisted decision support (with consideration of knowledge-based approaches) for data analysis problems. First, we discuss some situations where it is obviously appropriate to apply computer-assisted decision support in connection with data analysis tasks. Then, a brief historical retrospect is given viewing the development of this area of research and its interfaces to knowledge-based approaches. Against this background we illustrate two prototypes of knowledge-based decision support systems for specific data-analysis problems related to fields of interest of our own. Finally, we indicate possible progress and future activities in this area.     

Correlation when data are missing

Variable correlation is important for many operations research models. Manyinventory, revenue management, and queuing models presume uncorrelated demandbetween products, market segments, or time periods. The specific model applied,or the resulting policies of a model, can differ drastically depending onvariable correlation. Having missing data are a common problem for the realworld application of operations research models. This work is at the junction ofthe two topics of correlation and missing data. We propose a test ofindependence between two variables when data are missing. The typical method fordetermining correlation with missing data ignores all data pairs in which onepoint is missing. The test presented here incorporates all data. The test can beapplied when both variables are continuous, when both are discrete, or when onevariable is discrete and the other is continuous. The test makes no assumptionsabout the distribution of the two variables, and thus it can be used to extendapplication of non-parametric rank tests, such as Spearman's rankcorrelation, to the case where data are missing. An example is shown wherefailure to incorporate the incomplete data yields incorrect policies.     

Properties of large-scale TIDs observed in central China

This paper investigates the large scale travelling ionospheric disturbances (LSTIDs) using the observation data of an HF Doppler array located in Central China. The data observed in a high solar activity year (year 1989) are analyzed to obtain the main propagation parameters of LSTIDs such as period, horizontal phase velocity and propagating direction. Results are outlined as follows: Most of the LSTIDs propagate southward; others tend to propagate northward, mostly in summer; dispersion of most LSTIDs is matched with that of Lamb pseudomode, while others have the dispersion of long period gravity wave mode. The horizontal phase velocities of these two modes are about 220 and 450 m/s respectively. The analysis shows that LSTIDs are strongly pertinent to solar activity and space magnetic storms; thus the results presented here are significant for the research of ionospheric weather in mid-low latitude region.