Extension of multivariate regression trees to interval data. Application to electricity load profiling

Summary  Several data can be presented as interval curves where intervals reflect a within variability. In particular, this representation is well adapted for load profiles, which depict the electricity consumption of a class of customers. Electricity load profiling consists in assigning a daily load curve to a customer based on their characteristics such as energy requirement. Within the load profiling scope, this paper investigates the extension of multivariate regression trees to the case of interval dependent (or response) variables. The tree method aims at setting up simultaneously load profiles and their assignment rules based on independent variables. The extension of multivariate regression trees to interval responses is detailed and a global approach is defined. It consists in a first stage of a dimension reduction of the interval response variables. Thereafter, the extension of the tree method is applied to the first principal interval components. Outputs are the classes of the interval curves where each class is characterized both by an interval load profile (e.g. the class prototype) and an assignment rule based on the independent variables.     

随机删失场合半参数回归模型参数估计的重对数律‘

本文探讨了随机删失场合半参数回归模型的参数估计问题.考虑半参数回归模型Y =X}}3 + g(T)十。，其中(X,T)’为取值于Kp X [0,1〕上的随机向量，月为1'维未知参数向量，8为定义于【0.1]上的未知函数，。为随机误差，Ee = 0 . Eez = az }。未知，且(X ,T)与。独立，).被一个与之独立的随机变量V所截.此时仅能观察到:Z=min(Y,V),o=1(Y簇V)，参数I3,az的估计量禽及公 z可综合非参数的权函数估计法与参数的最小二乘估计方法得到.本文对核函数的情形得到了念及ar z的精确收敛速度即重对数律.     

左截断右删失下核密度估计的L1距离的界

本文在左截断右删失数据下获得了概论密度的核估计的L1距离的一个上界．     

浅议物理实验教学环节改革

为进一步提高大学物理实验的教学质量，本文就实验课的预习、操作、实验报告的撰写及考核方法进行了探讨。     

体全息存储系统读出图像的放大率的补偿算法研究

在1∶1体全息存储系统的读出过程中,保证图像的不失真和降低误码率是最关键的问题。提出了一种新颖的放大率补偿算法,定量分析了放大率偏移误差对图像的影响,并通过理论推导和实验模拟证明了这一算法的可行性。     

Recent developments in the statistical processing of textual data

Statisticians are accustomed to processing numerical, ordinal or nominal data. In many circumstances, such as socio-economic, epidemiologic sample surveys and documentary data bases, this data is juxtaposed with textual data (for example, responses to open questions in surveys). This article presents a series of language-independent procedures based upon applying multivariate techniques (such as correspondence analysis and clustering) to sets of generalized lexical profiles. The generalized lexical profile of a text is a vector whose components are the frequencies of each word (graphical form) or ‘repeated segment’ (sequence of words appearing with a significant frequency in the text). The processing of such large (and often sparse) vectors and matrices requires special algorithms. The main outputs are the following: (1) printouts of the characteristic words and characteristic responses for each category of respondent (these categories are generally derived from available nominal variables); (2) graphical displays of the proximities between words or segments and categories of respondents; (3) when analysing a combination of several texts: graphical displays of proximities between words or segments and each text, or between words or segments and groupings of texts. The systematic use of ‘repeated segments’ provides a valuable help in interpreting the results from a semantic point of view.     

光盘阵列系统的一种性能优化方法

提出了一种用低性能驱动器组建高性能光盘阵列系统性能的优化方法。针对传统的低性能光盘驱动器(驱动器内部数据传输率及接口速率均较低),提出了一种新型的数据传输模式。采用此优化方法可以使光盘阵列系统的整体性能得到较大地提高。同时还可以提高系统整体的性价比。     

Validating DEA as a ranking tool: An application of DEA to assess performance in higher education

There is a general interest in ranking schemes applied to complex entities described by multiple attributes. Published rankings for universities are in great demand but are also highly controversial. We compare two classification and ranking schemes involving universities; one from a published report, ‘Top American Research Universities’ by the University of Florida's TheCenter and the other using DEA. Both approaches use the same data and model. We compare the two methods and discover important equivalences. We conclude that the critical aspect in classification and ranking is the model. This suggests that DEA is a suitable tool for these types of studies.     

Reformulation of the support set selection problem in the logical analysis of data

The paper is concerned with the problem of binary classification of data records, given an already classified training set of records. Among the various approaches to the problem, the methodology of the logical analysis of data (LAD) is considered. Such approach is based on discrete mathematics, with special emphasis on Boolean functions. With respect to the standard LAD procedure, enhancements based on probability considerations are presented. In particular, the problem of the selection of the optimal support set is formulated as a weighted set covering problem. Testable statistical hypothesis are used. Accuracy of the modified LAD procedure is compared to that of the standard LAD procedure on datasets of the UCI repository. Encouraging results are obtained and discussed.     

The Method of Virtual Experts in Mathematical Diagnostics

Mathematical Diagnostics (MD) deals with identification problems arising in different practical areas. Some of these problems can be described by mathematical models where it is required to identify points belonging to two or more sets of points. Most of the existing tools provide some identification rule (a classifier) by means of which a given point is assigned (attributed) to one of the given sets. Each classifier can be viewed as a virtual expert. If there exist several classifiers (experts), the problem of evaluation of experts’ conclusions arises. In the paper for the case of supervised classification the method of virtual experts (the VE-method) is described. Based on this method, a generalized VE method is proposed where each of the classifiers can be chosen from a given family of classifiers. As a result, a new optimization problem with a discontinuous functional is stated. Examples illustrating the proposed approach are provided. The work of the second author was supported by the Russian Foundation for Fundamental Studies (RFFI) under Grant No 03-01-00668.