熵值赋权法的改进

熵值法是一种常见的赋权方法,在综合评价中有这较广泛的应用.但是样本出现异常点数据(如数值等于零的数据)时,熵值法就不能够很好地处理,这在很大程度上限制了熵值法的使用范围.文章将功效系数法和标准化方法相结合,对熵值法进行改进,扩展了熵值法的应用范围.     

考虑跨期变动趋势的改进熵权法研究

权重确定是多指标综合评价过程中的关键环节。文章针对主观赋权法中专家对指标重要性判断的随意性及常规客观赋权法的片面性,提出考虑跨期变动趋势的改进熵值赋权方法。该方法考虑了时间因素,从指标数据跨时期的变动趋势中挖掘指标的相对重要性信息,并基于熵理论确定指标的重要程度之比,分层次规划求解各指标权重。最后结合我国新型城镇化水平评价案例,验证了该方法的合理性和有效性。     

用熵值法进行综合评价如何处理极端值

在多指标综合评价中，确定指标权重的方法主要有主观赋权法和客观赋权法．主观赋权法是一类根据评价者主观上对各指标的重视程度来决定权重的方法；客观赋权法所依据的赋权原始信息来源于客观环境，它根据各指标的联系程度或各指标所提供的信息量染决定指标的权重．客观同权法有烟值法、主成分分析法、因子分析法、复相关系数法等等，本文针对均值法在应用中遇到的一些问题提出解决办法。一、熵值法的原理设有m个待评方案，n项评价指标，形成原始指标数据矩阵x=（Xij）mxn,对于某项指标xi，指标值xij的差距越大，则该指标在综合评价中所起…     

熵值法中无量纲化方法优良标准的探讨

在综合评价中,基于"差异驱动"原理的熵值法得到了广泛的应用,随之而来的是应用各种无量纲化方法对其进行改进。由于无量纲化方法有其各自的特点和适用范围,因此其各自的改进熵值法不可避免地存在优良性差异。文章通过理论推导与实证分析,对各改进熵值法的优良性进行探讨,最终得到最优的方法——极值熵值法,即极值处理法和熵值法的结合。     

城市低碳发展的熵值-灰色系统评判模型

文章在构建城市低碳发展评价指标的基础上，针对城市低碳发展评判中的模糊性和灰性特征，将熵值理论引入城市低碳发展评判中，构建了城市低碳发展熵值-灰色系统评判模型．对1990～2009年上海市的城市低碳发展水平作出了评价。同时，采用截面数据对北京、重庆、天津以及上海这四个直辖市的城市低碳发展状况进行了评价检验，并利用聚类分析法对四个直辖市的各指标数据进行聚类分析，证明所构建的城市低碳发展熵值-灰色系统评判模型是有效的。     

多层次人力资源绩效考评中指标权重的确定方法——AHP方法应用及其数据在excel中的处理

本文针对企业对员工绩效传统考核方法不科学,操作困难的问题,提出了一种运用AHP法结合考核数据在EXCEL中计算权值矩阵的可行方法。     

综合评价中的非线性优化层次分析法

在多指标综合评价问题中,涉及到的一个关键问题是各评价指标的权重的确定。目前,用来确定权重系数的方法有:层次分析法(AHP)、德尔菲法、模糊聚类法、灰色关联法、熵值法、人工神络定权法、因子分析法和路径分析法等。其中层次分析法是目前较常用的方法之一,它是系统工程中十分典型的定性分析和定理分析综合集成的方法。     

基于粗集-熵值法的高新技术企业自主创新能力评价

结合粗糙集属性约简算法及熵值法对高新技术企业自主创新能力进行综合评价。对××市高新技术企业进行的实例分析结果表明:粗糙集属性约简能有效剔除冗余指标,从而减少了后续计算量;熵值法能客观确定出指标权重,使综合评价结果更贴近现实。     

中国食品产业集群可持续发展潜力的综合评价

文章基于中国食品产业发展现状,构建了中国食品产业集群可持续发展潜力评价指标体系,根据区位熵理论识别和测度了中国食品产业集群,基于2000~2013年的面板数据,运用改进的熵值法计算得到了各个指标的权重,进而对中国食品产业集群的可持续发展潜力进行了分类评价和综合评价。     

区间数指标权重确定的熵值法改进

文章针对权重信息未知的区间数多指标决策中指标的客观赋权问题,研究了已有指标权重客观确定方法的不足。依据客观赋权的原理和序列信息熵的含义,利用各方案区间数指标值的上、下界序列信息熵,给出了确定各区间数指标权重的一种熵值法。     

Analyzing supersaturated designs with entropic measures

A supersaturated design is a design for which there are fewer runs than effects to be estimated. In this paper, we propose a method for screening out the important factors from a large set of potentially active variables, based on an information theoretical approach. Three entropy measures: Rényi entropy, Tsallis entropy and Havrda–Charvát entropy, have been associated with the measure of information gain, in order to identify the significant factors using data and assuming generalized linear models. The investigation of the proposed method performance and the comparison of each entropic measure application have been accomplished through simulation experiments. A noteworthy advantage of this paper is the use of generalized linear models for analyzing data from supersaturated designs, a fact that, to the best of our knowledge, has not yet been studied.     

The Gamma–Kumaraswamy distribution: An alternative to Gamma distribution

In this article, a new generalization of the Kumaraswamy distribution, namely the Gamma–Kumaraswamy distribution, is defined and studied. Various properties of the Gamma–Kumaraswamy are obtained. The structural analysis of the distribution in this article includes limiting behavior, mode, quantiles, moments, skewness, kurtosis, Shannon’s entropy, and order statistics. The method of maximum likelihood estimation is proposed for estimating the model parameters. For illustrative purposes, two real datasets are analyzed as application of the Gamma–Kumaraswamy distribution.     

On the Choice of the Ridge Parameter: A Maximum Entropy Approach

In this article, a new method to estimate the ridge parameter, based on the ridge trace and an analytical method borrowed from maximum entropy, is presented. The performance of the new estimator is illustrated through a Monte Carlo simulation study and an empirical application to the well-known Portland cement data set.     

Maximum entropy estimation of income share function from generalized Gini index

Following Sir Anthony and Atkinson who started thinking about the insensitivity of the Gini index to income shares of the lower and the upper income groups, a generalization of the classical Gini index was introduced by Kakwani, Donaldson, Weymark and Yitzhaki which is sensitive to both high and low incomes. In this paper, the maximum entropy method is used to estimate the underlying true income share function based on the limited information of the generalized Gini index about the income shares of a population's percentiles. The income share function is estimated through maximizing both the Shannon entropy and the second-order entropy. In the end, through parametric bootstrap and analyzing a real dataset, the results are compared with the estimator of the share function, which is obtained based on the total information. In contrast to the classic Gini index, the derived share function based on the generalized Gini index provides more accurate approximations for income shares of the lower and the upper percentiles.     

Bootstrapping one way analysis of rao's quadratic entropy

We establish a bootstrap approximation for the one way analysis of quadratic entropy in this paper. The quadratic entropy was introduced by Rao in 1982. It generalizes the concepts of variance and diversity measures and is useful in statistical inference. Our results in this paper provide a computational method to get rid of the nuisance parameters in the asymptotic distribution of the analysis of quadratic entropy (ANOQE) statistic.     

Computational Method for Jackknifed Generalized Ridge Tuning Parameter based on Generalized Maximum Entropy

In this article, a new method to estimate the Jackknifed generalized ridge tuning parameter, based on the Jackknifed Ridge-trace and an analytical method borrowed from generalized maximum entropy, is presented. The ideas in the article are illustrated and evaluated using to the well-known Portland cement data set and simulations.     

熵理论中熵及熵权计算式的不足与修正

简述了熵的概念和熵在社会科学中的应用,分析了熵理论中熵值及熵权传统计算式中存在的不足,提出了一种合理可行的修正方法,并对熵值及熵权计算式的修正进行了数学证明,有效地完善和发展了熵理论。     

Entropy estimation and goodness-of-fit tests for the inverse Gaussian and Laplace distributions using paired ranked set sampling

ABSTRACT

In this paper, Vasicek [A test for normality based on sample entropy. J R Stat Soc Ser B. 1976;38:54–59] entropy estimator is modified using paired ranked set sampling (PRSS) method. Also, two goodness-of-fit tests using PRSS are suggested for the inverse Gaussian and Laplace distributions. The new suggested entropy estimator and goodness-of-fit tests using PRSS are compared with their counterparts using simple random sampling (SRS) via Monte Carlo simulations. The critical values of the suggested tests are obtained, and the powers of the tests based on several alternatives hypotheses using SRS and PRSS are calculated. It turns out that the proposed PRSS entropy estimator is more efficient than the SRS counterpart in terms of root mean square error. Also, the proposed PRSS goodness-of-fit tests have higher powers than their counterparts using SRS for all alternative considered in this study.     

Maximum entropy sampling and optimal Bayesian experimental design

When Shannon entropy is used as a criterion in the optimal design of experiments, advantage can be taken of the classical identity representing the joint entropy of parameters and observations as the sum of the marginal entropy of the observations and the preposterior conditional entropy of the parameters. Following previous work in which this idea was used in spatial sampling, the method is applied to standard parameterized Bayesian optimal experimental design. Under suitable conditions, which include non-linear as well as linear regression models, it is shown in a few steps that maximizing the marginal entropy of the sample is equivalent to minimizing the preposterior entropy, the usual Bayesian criterion, thus avoiding the use of conditional distributions. It is shown using this marginal formulation that under normality assumptions every standard model which has a two-point prior distribution on the parameters gives an optimal design supported on a single point. Other results include a new asymptotic formula which applies as the error variance is large and bounds on support size.