基于变异系数-G1法的混合交叉赋权方法

文章利用评价指标的变异系数代替决策者主观人为确定相邻评价指标的重要性程度之比,构造了一种新的基于主观赋权的G1法与客观赋权的变异系数法有效巧妙结合的混合交叉赋权方法,该方法不仅避免了单一赋权方法无法反映客观数据信息或决策者主观意图的弊端,而且避免了组合赋权方法无法客观确定组合系数的双重难题.最后将新构造的混合交叉赋权方法应用于区域科技发展的综合评价.     

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。     

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。     

经济对外开放度指数的测算模型

文章从对外贸易、对外金融、对外经济合作、对外投资、国际旅游五个一级指标入手,结合层次分析法和主成分分析法,建立最优组合赋权模型,进行对外开放度的综合评价。该模型将主观赋权法和客观赋权法结合,使得评价结果更加符合客观实际。     

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…     

基于离差最大化和OWGA算子的多属性群决策方法

本文将离差最大化法和有序加权几何平均(OWGA)算子相结合,先根据群决策者对方案属性的客观评价值,基于离差最大化法计算出属性的权重,然后利用OWGA算子对群体决策信息进行集结,据此给出了多方案间的排序,该方法充分发挥了离差最大法的客观赋权性和OWGA算子的有效集结性,最后将该方法应用于金融机构风险监管系统评价中,实例表明了该方法的可行性和实用性。     

一种集成的生产系统有效性综合评价方法

本文从目前各种评价方法中选择和引入了主观赋权法和客观赋权法中的3种适宜方法:线性加权法、主成分分析(因子分析)法和标准化法,给出了适应于每种方法的计算方法,分析了每种方法优势和不足之处。并在此基础上提出了融合主观赋权法和客观赋权法优势的集成方法,从而使评价结果更为合理,此方法也适合应用于各种综合评价类型中。     

基于CRITIC的多属性决策组合赋权方法

多属性决策中属性权重的确定方法有多种,每种赋权方法有其各自的特点,为了综合利用各种赋权法的优势,文章基于CRITIC的基本原理,提出了一种组合赋权方法。该方法通过对利用比强度和冲突性结合构造组合权重,研究了模型的求解,给出权重的计算公式,探讨组合赋权方法的检验,确保组合权重能反映主观信息和客观信息,并总结该方法的计算步骤。最后进行实例分析,结果表明该组合赋权方法是有效性的。     

一种群组决策中专家权重确定的方法与应用

本文针对群组决策中多名专家赋权且赋权结果不一致的情况,借助变异系数法和均值的思想,给出了一种确定专家决策权重的方法。该方法的特点在于依据专家与群组其他专家赋权结果以及与群组专家赋权结果平均值之间的一致性程度来确定专家的权重。该方法能够充分利用原始决策信息,从而使评价结果更加客观,因而具有较大的实用价值。最后,通过一个例子说明了该方法的实用性。     

基于组合赋权法的企业技术创新能力评价

为了应对工业4.0带来的挑战,新兴企业应当重点发展技术创新能力,因此采用综合方法对企业技术创新能力进行评价尤为重要.在复杂的技术创新能力评价中,需要考虑主观和客观两方面因素,文章首先从创新资源投入能力、创新实施能力和创新产出能力三个方面构建了评价指标体系;然后基于评价指标体系的构成,结合主客观赋权法评价的优点,采用组合赋权法对技术创新能力进行评价;最后通过算例说明了组合赋权评价法的有效性.     

Olympic rankings based on objective weighting schemes

In this paper, we propose an objective principal components weighting scheme for all-time Winter Olympic gold, silver and bronze medals based solely on the number of medals won. Our results suggest that the approximately equal weights be assigned (or the total medal counts be used regardless of color) if all of the three medal types are retained for ranking purposes. When the proposed methodology is tested against five alternative weighting schemes that have been suggested in the literature using the results for the 2010 Vancouver Winter Olympics, we find a significant agreement in the country rankings. Furthermore, our implementation of principal components variable reduction strategy results in the identification of silver as the best representative medal count for parsimonious Winter Olympics rankings.KEYWORDS: Olympic rankings, principal components analysis, variable reduction strategy, medal counts, objective weighting schemeJEL Classifications: C18, C38, C43     

一种加权主成分距离的聚类分析方法

指标之间的高度相关性及其重要性差异导致了传统聚类分析方法往往无法获得良好的分类效果。本文在对传统聚类分析方法及其各种改进方法局限性展开探讨的基础上,运用数学方法重构了分类定义中的距离概念,通过定义自适应赋权的主成分距离为分类统计量,提出一种新的改进的主成分聚类分析方法——加权主成分距离聚类分析法。理论研究表明,加权主成分距离聚类分析法系统集成了已有聚类分析方法的优点,有充分的理论基础保证其科学合理性。仿真实验结果显示,加权主成分距离聚类分析法能够有效解决已有聚类分析方法在特定情形下的失真问题,所得分类效果更为理想。     

专家咨询约束下的最大方差权数计算法

专家咨询约束下的最大方差权数计算法姜国麟刘弘朱平芳ＡＢＳＴＲＡＣＴＡｍｅｔｈｏｄｔｏｄｅｔｅｒｍｉｎｅｔｈｅｗｅｉｇｈｔｓｉｎａｃｏｍｐｏｓｉｔｅｅｖａｌｕａｔｉｏｎｏｆｍｕｌｔｉｎｄｉｃａｔｏｒｓｉｓａｄｄｒｅｓｓｅｄ．Ｔｈｉｓｍｅｔｈｏｄｏｖｅｒ...     

基于动态时间规整的面板数据聚类方法研究及应用

以往的面板数据聚类方法存在一些缺陷,有必要基于动态时间规整的思路进一步改进与完善面板数据聚类方法。利用国家级经济技术开发区数据开展的实证分析结果表明：新的方法既能够很好地反映面板数据的动态变化、又避免了已有的面板数据聚类方法中各种距离如何赋权的问题,聚类结果较为稳定且有很好的可视化效果。     

GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study

We examine alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo methods. We document the existence of a tradeoff between the number of moments, or information, included in estimation and the quality, or precision, of the objective function used for estimation. Furthermore, an approximation to the optimal weighting matrix is used to explore the impact of the weighting matrix for estimation, specification testing, and inference procedures. The results provide guidelines that help achieve desirable small-sample properties in settings characterized by strong conditional heteroscedasticity and correlation among the moments.     

一种基于粗糙集理论的组合预测方法

一、前言组合预测方法的基本原理是博采众多预测方法的优势 ,将多个不同预测模型的预测信息组合以期有效地改善预测模型的拟合能力 ,提高预测精度。而组合信息的一般方式是对各个预测模型进行线性加权平均[1～ 3 ] 。因此 ,组合预测方法的研究工作大部分集中在权系数的确定方法上 ,由此产生了许多组合预测方法。常见的方法有 :基于专家意见、相关分析、误差和最小、模糊数学、灰色理论、人工神经网络、遗传算法、小波分析等的组合预测方法。专家意见法的致命弱点是过分依赖专家的主观判断和经验 ,其结果有时难以令人信服。基于相关分析、误…     

Alternative GMM estimators for first-order autoregressive panel model: An improving efficiency approach

This article considers first-order autoregressive panel model that is a simple model for dynamic panel data (DPD) models. The generalized method of moments (GMM) gives efficient estimators for these models. This efficiency is affected by the choice of the weighting matrix that has been used in GMM estimation. The non-optimal weighting matrices have been used in the conventional GMM estimators. This led to a loss of efficiency. Therefore, we present new GMM estimators based on optimal or suboptimal weighting matrices. Monte Carlo study indicates that the bias and efficiency of the new estimators are more reliable than the conventional estimators.     

Handling missing data by deleting completely observed records

When data are missing, analyzing records that are completely observed may cause bias or inefficiency. Existing approaches in handling missing data include likelihood, imputation and inverse probability weighting. In this paper, we propose three estimators inspired by deleting some completely observed data in the regression setting. First, we generate artificial observation indicators that are independent of outcome given the observed data and draw inferences conditioning on the artificial observation indicators. Second, we propose a closely related weighting method. The proposed weighting method has more stable weights than those of the inverse probability weighting method (Zhao, L., Lipsitz, S., 1992. Designs and analysis of two-stage studies. Statistics in Medicine 11, 769–782). Third, we improve the efficiency of the proposed weighting estimator by subtracting the projection of the estimating function onto the nuisance tangent space. When data are missing completely at random, we show that the proposed estimators have asymptotic variances smaller than or equal to the variance of the estimator obtained from using completely observed records only. Asymptotic relative efficiency computation and simulation studies indicate that the proposed weighting estimators are more efficient than the inverse probability weighting estimators under wide range of practical situations especially when the missingness proportion is large.     

Detecting clusters with increased mean using scan windows with variable radius

Applying spatiotemporal scan statistics is an effective method to detect the clustering of mean shifts in many application fields. Although several exponentially weighted moving average (EWMA) based scan statistics have been proposed, the existing methods generally require a fixed scan window size or apply the weighting technique across the temporal axis only. However, the size of shift coverage is often unavailable in practical problems. Using a mismatching scan radius may mislead the size of cluster coverage in space or delay the time to detection. This research proposed an stEWMA method by applying the weighting technique across both temporal and spatial axes with variable scan radius. The simulation analysis showed that the stEWMA method can have a significantly shorter time to detection than the likelihood ratio-based scan statistic using variable scan radius, especially when cluster coverage size is small. The application to detecting the increase of male thyroid cancer in the New Mexico state also showed the effectiveness of the proposed method.