基于妥协算法的区间数多属性决策新方法

妥协(VIKOR)算法往往用于精确数的评估中,但由于决策者的经验等原因,往往在决策时给出的评估值为区间数.文章针对这一情况.将VIKOR引入到区间数的评价中,利用区间数的特性分别计算出理想解和非理想解,以及方案综合评价值和利益比率;并应用区间数的可能度排序方法进行排序.实例证明,该方法是有效的.     

一种具有独立子系统的区间DEA评价与排序方法

具有独立子系统的决策单元的评价及排序问题是DEA研究领域的重要内容之一.文章在修正的Russell模型的基础上,提出了一种两阶段区间DEA方法,对具有不确定性且包含独立子系统的决策单元进行了相对有效性评价与排序.该方法丰富了DEA成果,有着较广的应用前景.     

基于分层效率的区间DEA方法研究

文章针对现有的区间DEA方法采用非统一指标进行效率评价的不足,提出一种基于分层效率的区间DEA方法。并该方法能有效得到多层效率前沿面及每层前沿面的有效决策单元,并可按效率前沿面层次实现决策单元的分类。在此基础上,文章进一步提出了各效率前沿面层内有效单元的排序方法;并采用一个算例分析说明了该方法的合理性和可行性。     

区间数序列处理方法新探

区间估计是专家评价中的一种重要方法,目前对给定为区间数的决策方法研究较多,而对区间数的处理大多基于均匀分布或正态分布。文章针对这种对区间数序列的分析和处理方法研究的不足,提出了一种新的基于β分布的处理方法。该方法通过用β分布对区间数序列分布进行拟合,从而解决了对区间数序列难以进行分析和处理这一难点,并通过案例验证了该方法。结果表明,该方法原理正确,简便可靠,在决策管理中具有广泛的应用前景和普适性。     

一种基于多维区间数可能度的投资决策方法

区间数是描述不确定性的一种数学方法。通常的不确定多属性决策方法基于一维区间数。基于多维区间数比较的可能度概念,文章提出了一种多维区间数排序的多属性投资决策方法。首先在介绍一维区间数和相关运算的基础上,分析了多维区间数概念及其相关运算,并给出多维区间数比较的可能度概念;然后类似于一维区间数比较的可能度方法,提出基于多维区间数可能度概念的多维区间数排序新方法,进一步研究了一种基于多维区间数可能度的不确定性投资决策排序方法,确定最优投资方案;最后给出一个算例说明了提出方法的有效性。     

一种新的基于TOPSIS的区间数排序法

针对有限个区间数的排序问题,提出了一种基于TOPSIS的区间数排序法。首先定义了有限个区间数的正理想区间数和负理想区间数;然后引入传统的TOPSIS思想,定义了区间数相对贴近度的概念,根据区间数的相对贴近度大小可以对任意有限个区间数进行排序。最后通过一个属性权重和属性值均为区间数的多属性决策问题说明了该方法的有效性和可行性。与传统方法比较,此方法具有原理简单,运算方便,实用性强等优点。     

一种面向属性残缺偏好效用矩阵的大群体决策方法

决策成员评价部分决策方案的大群体决策问题可视为属性残缺效用值矩阵下的大群决策问题.文章将残缺效用值转化为区间教形式的效用值,形成了大群体关于全部决策方案的区间数效用值矩阵;对大群体成员进行聚类,根据聚类结果确定成员权重,将该权重和区间数效用值矩阵合成获得了决策方案的排序向量;提出了群体意见反映度指标度量群决策结果反映群体意见的程度,对群决策结果进行评价;提出了成员意见差异度指标修改相应成员的效用值向量.     

一种区间型多属性决策的新方法

针对决策者的偏好信息和决策矩阵元素均为区间数的不确定多属性决策问题,提出了一种新的决策方法。该方法引入决策者的心态指标把区间型决策矩阵转化为带心态指标的决策矩阵,再通过求解主观偏好与客观偏好的总绝对偏差最小的单目标规划问题,得到属性的权重向量,利用方案的综合属性值给出各方案的排序结果。当决策者处于不同的心态时,可以通过调整其心态指标来进行决策,更加符合实际,计算简单,易于计算机上实现,应用实例表明了方法的有效性和实用性。     

基于语言评价信息视角的PSS产品开发方案决策评价

文章以语言信息评价为切入点对PSS方案决策评价方法进行了研究,提出了将定量评价信息与定性评价信息相融合的系统化决策模型体系.通过将语言变量模糊化与定量评价指标无量纲化将定量与定性语言评价指标相集成,形成聚合评价模糊数,最后在Bass-Kwakernaak排序法的基础上运用Borda函数得出方案最终排序.案例证明了该方法的有效性.     

基于前景理论的区间数多准则决策方法

针对准则偏好值为区间数的不确定性决策问题,提出了一种基于前景理论的多准则决策方法。该方法将准则值看作给定区间内N个等差随机数,利用正态分布的分布函数来表示区间内准则值的分布规律,在给定各准则参考点的基础上,通过价值函数和决策权重函数计算方案在各准则下的前景值,并通过加权平均得到各方案的总前景值;根据前景值的大小对所有方案排序并得到最优方案。该方案的可行性通过一个简单实例得到了论证。     

多种综合评价方法的优劣判断研究

一、评价方法优劣判据的提出对于多指标多对象的综合评价 ,人们已提出许多不同的综合评价方法。传统评价方法有综合评分法、标准化计分法和功效系数法 ;多目标决策评价方法有优序法、引进次序法、双基点法和投影法 ;现代评价方法有主成分分析法 ,因子分析法、熵值法、模糊评价法、灰色关联分析评价法和层次分析评价法等。这些方法的应用相当活跃。但由于各种评价方法对原始数据的处理、权数的确定、评价方法本身掌握的标准和计算方法的不同 ,使评价结果存在着差异。这就向我们提出一个问题 ,即如何对各种综合评价方法进行优劣判断。我们认为…     

非线性协整的秩检验方法及其响应面函数研究

 本文对非线性协整关系的秩检验方法进行了系统的梳理,运用Monte Carlo模拟给出了不同样本容量的各个秩检验统计量的临界值,并进一步探讨了其响应面函数,给出了各个秩检验统计量临界值的近似计算公式。对中国上证综指与主要发达国家股指关系的秩协整检验表明,与传统线性协整Johansen检验相比,秩协整检验能够检测到更多的线性和非线性协整关系。     

Robust rank screening for ultrahigh dimensional discriminant analysis

In this paper, we consider sure independence feature screening for ultrahigh dimensional discriminant analysis. We propose a new method named robust rank screening based on the conditional expectation of the rank of predictor’s samples. We also establish the sure screening property for the proposed procedure under simple assumptions. The new procedure has some additional desirable characters. First, it is robust against heavy-tailed distributions, potential outliers and the sample shortage for some categories. Second, it is model-free without any specification of a regression model and directly applicable to the situation with many categories. Third, it is simple in theoretical derivation due to the boundedness of the resulting statistics. Forth, it is relatively inexpensive in computational cost because of the simple structure of the screening index. Monte Carlo simulations and real data examples are used to demonstrate the finite sample performance.     

模糊Borda法的缺陷分析及其改进思路

 通过对模糊Borda法的分析，笔者认为该方法在技术处理上偏离了评价的基本思想，根本无法实现将评价值和评价序相结合的初衷。为了实现将两者相结合的目的，本文提出了两个解决思路：利用被评价单位在评价值的差距计算相对位次；或计算名次转换值与评价值的交叉乘积。     

Model averaging based on rank

In this paper, we investigate model selection and model averaging based on rank regression. Under mild conditions, we propose a focused information criterion and a frequentist model averaging estimator for the focused parameters in rank regression model. Compared to the least squares method, the new method is not only highly efficient but also robust. The large sample properties of the proposed procedure are established. The finite sample properties are investigated via extensive Monte Claro simulation study. Finally, we use the Boston Housing Price Dataset to illustrate the use of the proposed rank methods.     

A test of concordance based on Gini’s mean difference

A new rank correlation index, which can be used to measure the extent of concordance or discordance between two rankings, is proposed. This index is based on Gini’s mean difference computed on the totals ranks corresponding to each unit and it turns out to be a special case of a more general measure of the agreement of m rankings. The proposed index can be used in a test for the independence of two criteria used to rank the units of a sample, against their concordance/discordance. It can then be regarded as a competitor of other classical methods, such as Kendall’s tau. The exact distribution of the proposed test-statistic under the null hypothesis of independence is studied and its expectation and variance are determined; moreover, the asymptotic distribution of the test-statistic is derived. Finally, the implementation of the proposed test and its performance are discussed. Both the authors contributed equally to this work; however, the actual writing of the paper was as follows: Sects. 2 and 3 are due to C. G. Borroni, Sects. 1 and 4 are due to M. Zenga.     

Rank estimation of regression coefficients using iterated reweighted least squares

This paper is concerned with the rank estimator for the parameter vector β in a linear model which is obtained by the minimization of a rank dispersion function. The rank estimator has many advantages over the regular least squares estimator, but the inaccessibility of software to carry out its computation has limited its use. An iterated reweighted least squares algorithm is presented for the computation of the rank estimator. The method is simple in concept and can be carried out readily with a wide variety of statistical software. Details of the method are discussed along with some results on its asymptotic distribution and numerical stability. Some examples are presented to show advantages of the rank method.     

Robust adaptive model selection and estimation for partial linear varying coefficient models in rank regression

Partial linear varying coefficient models are often used in real data analysis for a good balance between flexibility and parsimony. In this paper, we propose a robust adaptive model selection method based on the rank regression, which can do simultaneous coefficient estimation and three types of selections, i.e., varying and constant effects selection, relevant variable selection. The new method has superiority in robustness and efficiency by inheriting the advantage of the rank regression approach. Furthermore, consistency in the three types of selections and oracle property in estimation are established as well. Simulation studies also confirm our method.     

Sample Size Calculation for Rank Tests Comparing K Survival Distributions

Rank tests, such as logrank or Wilcoxon rank sum tests, have been popularly used to compare survival distributions of two or more groups in the presence of right censoring. However, there has been little research on sample size calculation methods for rank tests to compare more than two groups. An existing method is based on a crude approximation, which tends to underestimate sample size, i.e., the calculated sample size has lower power than projected. In this paper we propose an asymptotically correct method and an approximate method for sample size calculation. The proposed methods are compared to other methods through simulation studies.     

Asymptotic separability in sensitivity analysis

In an observational study in which each treated subject is matched to several untreated controls by using observed pretreatment covariates, a sensitivity analysis asks how hidden biases due to unobserved covariates might alter the conclusions. The bounds required for a sensitivity analysis are the solution to an optimization problem. In general, this optimization problem is not separable, in the sense that one cannot find the needed optimum by performing a separate optimization in each matched set and combining the results. We show, however, that this optimization problem is asymptotically separable, so that when there are many matched sets a separate optimization may be performed in each matched set and the results combined to yield the correct optimum with negligible error. This is true when the Wilcoxon rank sum test or the Hodges-Lehmann aligned rank test is applied in matching with multiple controls. Numerical calculations show that the asymptotic approximation performs well with as few as 10 matched sets. In the case of the rank sum test, a table is given containing the separable solution. With this table, only simple arithmetic is required to conduct the sensitivity analysis. The method also supplies estimates, such as the Hodges-Lehmann estimate, and confidence intervals associated with rank tests. The method is illustrated in a study of dropping out of US high schools and the effects on cognitive test scores.