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…     

基于结构CRITIC法的学术期刊评价指标的赋权方法及比较

在复杂、多元的期刊评价过程中经常涉及各评价指标的赋权问题。不同的评价指标赋权方案直接影响期刊评价的结果,因此如何确定指标权重是一个重要问题。文章基于CRITIC法的赋权原理,结合层次分析法,提出了一种全新的主客观赋权法相结合的权重确定方法,即结构CRITIC法。采用2022年《中国学术期刊影响因子年报（自然科学与工程技术）》中17种数学学术期刊的数据,使用包括原始的CRITIC法在内的其他三种赋权方法进行对比分析。结果表明,主观赋权法和客观赋权法赋权结果的差异性影响了评价结果的公认性;赋权方法的选择应当考虑其适用性;结构CRITIC法是在期刊评价中更能从主观和客观两个方面反映指标权重的计算方法。结构CRITIC法为学术期刊评价提供了一种新的赋权方法。     

统计学中的组合思想初探

不管有没有意识到，人们都在大量、广泛地使用着组合思想，可以说，组合思想已经渗透到各个领域。本文试图对统计学中的组合思想加以总结，并在此基础上对组合思想作一理论上的分析。 一、统计学中的组合思想 １组合评价 在多指标综合评价中，人们提出了各种各样的评价方法。按赋权方法的不同，单一综合评价方法可分为两大类：主观赋权评价法和客观赋权评价法。然而，无论是选用主观赋权评价法，还是选用客观赋权评价法进行综合评价，都有其自身无法解决的缺陷。主观赋权法虽然能充分吸收本领域专家高深的理论知识和丰富经验，体现出各个指…     

中国情境下世界一流制造企业评价体系研究

文章运用文献计量和扎根理论对世界一流制造企业的特征进行分析，构建了一个能有效评价世界一流制造企业的评价体系，包含资本规模、效益增长能力、创新能力、国际化能力和社会影响力5大维度共37个评价指标。通过综合运用层次分析法、熵权法和博弈论组合赋权法对指标赋权，并将评价体系应用于中国通信制造行业的评价中。     

和谐社会评价若干问题研究

 在对和谐社会的评价中仍然存在着一些值得进一步研究的问题，如：指标设计以及赋权、指标的定期实际测定、要不要提出和谐社会建设的目标值等。根据胡锦涛书记对和谐社会的阐述，同时考虑到可以定期从统计年鉴获得数据，从民主法治到公平正义、诚信友爱、充满活力、安定有序、人与自然和谐相处设计了18项指标，采用主成分法对和谐指标赋权，并对历年广东和谐社会进行实证综合评价。     

城市公共文化服务的综合评价模型

文章构建了区域公共文化服务评价指标体系,并根据AHP法和CRITIC法的优缺点,运用乘法集成赋权法对指标体系进行组合赋权,克服了单独使用主观赋权法和客观赋权法进行赋权的缺陷。在此基础上运用综合指标法,对京津沪渝四直辖市的城市公共文化服务进行综合评价。     

公司财务状况评价方法新探

公司财务指标中同类指标间相关性较强、不同类指标间相关性较弱、且不同类指标对总评分价值不同。根据这一特点 ,本文采用主观赋权法和客观赋权法相结合来对公司财务状况进行综合评价。评价分析的结果不是仅仅对所取样本进行排序 ,也不象某些主观评价法那样需要进行复杂繁琐的计算 ,而是运用统计知识得出一个简单易行的评分式和判别方法 ,从而便于使用者较容易的对某公司的财务状况进行判断。     

基于企业价值最大化的企业业绩综合评价

文章建立了以企业价值最大化为导向的企业业绩评价指标体系,构建静态评价和动态评价相结合的综合评价模型,以熵值法为静态评价指标进行赋权,以等差数列对动态评价的时间维度进行赋权,来综合评价企业业绩,并对我国家电行业上市公司进行实证研究。     

公司财务状况评价方法新探

公司财务指标中同类指标间相关性较强、不同类指标间相关性较弱、且不同类指标对总评分价值不同。根据这一特点 ,文章采用主观赋权法和客观赋权法相结合来对公司财务状况进行综合评价。评价分析的结果不是仅仅对所取样本进行排序 ,也不象某些主观评价法那样需要进行复杂繁琐的计算 ,而是运用统计知识得出一个简单易行的评分式和判别方法 ,从而便于使用者较容易的对某公司的财务状况进行判断。     

县域经济发展综合评价方法及其应用

综合评价结果往往取决于指标与权重的选取。文章依据科学发展观和相关经济原理选择一些能够反映社会经济以及人民生活水平的指标,分别利用定量赋权、定性与定量相结合的赋权方法对湖北省县域经济发展状况进行动态评价,并根据评价结果提出一些对策建议。     

银行信贷对经济结构影响的一般均衡效应分析：厦门案例

 本文研究金融结构变化对宏观经济结构和产业结构的影响，并以厦门为例，以银行信贷规模为切入点，通过研究银行信贷及其产业投向的变动对于厦门市经济发展与结构变迁的影响，从而洞察金融结构转换对厦门市经济发展与结构变迁的影响问题。     

Joint impact of multiple observations on a subset of variables in multiple linear regression

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE_ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F_j (Partial F-test for the jth variable) and F_j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE_IJ, F_J and F_J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.     

A new variable importance measure for random forests with missing data

Random forests are widely used in many research fields for prediction and interpretation purposes. Their popularity is rooted in several appealing characteristics, such as their ability to deal with high dimensional data, complex interactions and correlations between variables. Another important feature is that random forests provide variable importance measures that can be used to identify the most important predictor variables. Though there are alternatives like complete case analysis and imputation, existing methods for the computation of such measures cannot be applied straightforward when the data contains missing values. This paper presents a solution to this pitfall by introducing a new variable importance measure that is applicable to any kind of data—whether it does or does not contain missing values. An extensive simulation study shows that the new measure meets sensible requirements and shows good variable ranking properties. An application to two real data sets also indicates that the new approach may provide a more sensible variable ranking than the widespread complete case analysis. It takes the occurrence of missing values into account which makes results also differ from those obtained under multiple imputation.     

APPLE: approximate path for penalized likelihood estimators

In high-dimensional data analysis, penalized likelihood estimators are shown to provide superior results in both variable selection and parameter estimation. A new algorithm, APPLE, is proposed for calculating the Approximate Path for Penalized Likelihood Estimators. Both convex penalties (such as LASSO) and folded concave penalties (such as MCP) are considered. APPLE efficiently computes the solution path for the penalized likelihood estimator using a hybrid of the modified predictor-corrector method and the coordinate-descent algorithm. APPLE is compared with several well-known packages via simulation and analysis of two gene expression data sets.     

Evaluating the impact of a grouping variable on Job Satisfaction drivers

An important issue in Job Satisfaction analysis is to discover the most important drivers of the workers’ overall satisfaction. This can be investigated by means of data mining techniques able to measure the importance of a covariate in the prediction of a given outcome. Variable importance measures are mainly proposed in the literature in the framework of tree-based learning ensembles, like Random Forests or Gradient Boosting Machine. In this paper a Random Forest variable importance measure is used for mining the drivers of Job Satisfaction in the Social Service sector. In addition an innovative algorithmic procedure is proposed in order to assess the impact of a grouping variable on this variable importance measure. The goal is to investigate if the importance of a Job Satisfaction driver is different for subjects belonging to different groups.     

A Note on a Partition of the Likelihood Ratio Test for Autoregressive Covariance Structure

A sequence of nested hypotheses is presented for the examination of the assumption of autoregressive covariance structure in, for example, a repeated measures experiment. These hypotheses arise naturally by specifying the joint density of the underlying vector random variable as a product of conditional densities and the density of a subset of the vector random variable. The tests for all but one of the nested hypotheses are well known procedures, namely analysis of variance F-tests and Bartlett's test of equality of variances. While the procedure is based on tests of hypotheses, it may be viewed as an exploratory tool which can lead to model identification. An example is presented to illustrate the method.     

Bounds on dispersion of order statistics based on dependent symmetrically distributed random variables

We consider a fixed number of arbitrarily dependent random variables with a common symmetric marginal distribution. For each order statistic based on the variables, we determine a common optimal bound, dependent in a simple way on the sample size and number of order statistics, for various measures of dispersion of the order statistics, expressed in terms of the same dispersion measure of the single original variable. The dispersion measures are connected with the notion of M-functional of a random variable location with respect to a symmetric and convex loss function. The measure is defined as the expected loss paid for the discrepancy between the M-functional and the variable. The most popular examples are the median absolute deviation and variance.     

Measures of fit in principal component and canonical variate analyses

Treating principal component analysis (PCA) and canonical variate analysis (CVA) as methods for approximating tables, we develop measures, collectively termed predictivity, that assess the quality of fit independently for each variable and for all dimensionalities. We illustrate their use with data from aircraft development, the African timber industry and copper froth measurements from the mining industry. Similar measures are described for assessing the predictivity associated with the individual samples (in the case of PCA and CVA) or group means (in the case of CVA). For these measures to be meaningful, certain essential orthogonality conditions must hold that are shown to be satisfied by predictivity.     

Inference from Accelerated Degradation and Failure Data Based on Gaussian Process Models

An important problem in reliability and survival analysis is that of modeling degradation together with any observed failures in a life test. Here, based on a continuous cumulative damage approach with a Gaussian process describing degradation, a general accelerated test model is presented in which failure times and degradation measures can be combined for inference about system lifetime. Some specific models when the drift of the Gaussian process depends on the acceleration variable are discussed in detail. Illustrative examples using simulated data as well as degradation data observed in carbon-film resistors are presented.