评分的客观权重：自动民主方法
Objective Weights for Scoring: The Automatic Democratic Method
AI总结：
本文提出一种基于DEA上界得分和最小二乘回归的客观赋权评分方法，从全体被评估实体数据中自动生成民主的评分公式，避免主观判断。
摘要：
在比较产品、服务、实体等的表现时，会涉及多个属性。本文研究在寻求总体评分时如何为这些属性赋权，提出一种在评分公式中生成权重的客观方法，避免个人判断。第一步是找出每个被评估实体的最大可能得分，这些上界得分通过数据包络分析（Data Envelopment Analysis，DEA）求得。第二步通过将唯一的DEA得分对属性数据进行回归，得到评分公式中的权重。文中给出了使用最小二乘法并避免其他距离度量的理由。该方法在真实得分和权重已知的数据上进行了测试。该方法能够构建一个客观的评分公式，该公式由所有被评估实体的数据生成，因此在这个意义上是民主的。
英文摘要：
When comparing performance (of products, services, entities, etc.), multiple attributes are involved. This paper deals with a way of weighting these attributes when one is seeking an overall score. It presents an objective approach to generating the weights in a scoring formula which avoids personal judgement. The first step is to find the maximum possible score for each assessed entity. These upper bound scores are found using Data Envelopment Analysis. In the second step the weights in the scoring formula are found by regressing the unique DEA scores on the attribute data. Reasons for using least squares and avoiding other distance measures are given. The method is tested on data where the true scores and weights are known. The method enables the construction of an objective scoring formula which has been generated from the data arising from all assessed entities and is, in that sense, democratic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
