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中国管理科学 ›› 2022, Vol. 30 ›› Issue (11): 149-158.doi: 10.16381/j.cnki.issn1003-207x.2020.0969

• 论文 • 上一篇    下一篇

基于反映象相关矩阵的评价指标筛选方法研究

陈洪海   

  1. 南京财经大学金融学院,江苏 南京210023
  • 收稿日期:2020-05-25 修回日期:2020-10-11 出版日期:2022-11-20 发布日期:2022-11-28
  • 通讯作者: 陈洪海(1978-),男(汉族),辽宁辽中人,南京财经大学金融学院,讲师,博士,研究方向:金融风险管理、决策理论,Email:adams2009@163.com. E-mail:adams2009@163.com
  • 基金资助:
    国家自然科学基金资助重点项目 (71731003);国家社会科学基金资助重大项目 (17ZDA037);江苏省高校自然科学研究资助项目 (18KJB120003)

Method of Screening Evaluation Indicators Based on Anti-image Correlation Matrix

CHEN Hong-hai   

  1. School of Finance, Nanjing University of Finance and Economics, Nanjing 210023, China
  • Received:2020-05-25 Revised:2020-10-11 Online:2022-11-20 Published:2022-11-28
  • Contact: 陈洪海 E-mail:adams2009@163.com

摘要: 评价指标间信息重叠高会扭曲评价结果,而已有研究主要基于一个指标与部分指标间相关性降低指标集的信息重叠,指标剔除多且信息重叠降低得慢,极易导致评价信息过度损失。为此,本文首先引入取样适切性量数(MSA)表示一个评价指标与其余全部指标间信息重叠水平,引入KMO检验统计量表示一组评价指标整体信息重叠水平。在此基础上,依次剔除MSA值最大指标,直至剩余指标KMO值不高为止,快速降低评价指标集整体信息重叠。之后,通过剔除偏相关水平高的任两个指标中MSA值较大的一个指标,避免个别指标间信息重叠水平较高。最后,与目前应用最广泛的信息重叠指标筛选方法对比表明:本文方法降低指标集信息重叠效率更高,评价信息损失更少;既不会误删信息重叠水平低的评价指标,亦不会误保留信息重叠水平高的评价指标。

关键词: 评价指标筛选;信息重叠;综合评价;反映象相关矩阵;取样适切性量数

Abstract: Evaluation is the basis of scientific decision-making and the most common cognitive activity of human beings. Evaluation index system is the basis of evaluation activities, so it is very important to select evaluation indicators scientifically. A group of evaluation indicators constitute an interactive and interactive system, and there are complicated information overlapping among them. Moreover, the overlapping information will be repeatedly emphasized in the comprehensive evaluation, which will distort the evaluation results. Therefore, it is necessary to eliminate some indicators with high information overlap to reduce the information overlap level of indicator set. The existing research mainly solves this problem by reducing the correlation between one indicator and some (mainly another) indicators. It is very easy to eliminate more indicators, but the information overlap of indicator set decreases slowly, which leads to excessive loss of evaluation information. In order to solve the above problems, Measure of Sampling Adequacy (MSA) is first introduced to represent the information overlapping level between an evaluation indicator and all other indicators, and KMO test statistics is introduced to represent the overall information overlapping level of a group of evaluation indicators. Secondly, the indicators with the largest MSA value in the indicator set are removed in turn until the KMO value of the remaining indicators is not high, which can quickly reduce the overall information overlap of the evaluation indicator set. After that, one with higher MSA value is eliminated in any two indicators with high partial correlation level to avoid the high information overlap between individual indicators. Finally, taking a small enterprise loan customer credit evaluation index screening of a commercial bank in China as an example, compared with the most widely used screening methods of information overlap indicators, it is used to illustrate the feasibility and validity of the proposed method. The results show that this method has higher efficiency of reducing information overlap of indicator set and less loss of evaluation information; it can neither delete the evaluation indicators with low information overlap level nor retain the evaluation indicators with high information overlap level. The study of the evaluation indicator selection methods is expanded, and a reference and guidance for solving the problem of reducing the high correlation among indicators is provided.

Key words: evaluation indicator screening; information overlapping; comprehensive assessment;anti-image correlation matrix; measure of sampling adequacy

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