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Study on DEA method of multiple environmental character variable (ECV) grouping in DMU environment heterogeneity situation

机译:DMU环境异质性情况下多种环境特征变量(ECV)分组的DEA方法研究

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Classical DEA assumes DMU to be homogeneous, but in fact, some researchers found that DMU always had heterogeneous character, which would result in bias of relative efficiency scores and influence discrimination power of DEA. Environment heterogeneity is one characteristic manifestation of DMU heterogeneity. With retrospect to DEA method on DMU environment heterogeneity and focus on defect of the frontier separation approach, this paper discusses DEA method of multiple environmental character variable (ECV in short) grouping and demonstrates the method regarding China road freight transportation industry (CRFTI in short), and compares the result to the classical DEA method, not considering environment heterogeneity to confirm the discrimination power. The author wishes to provide some revelation to DEA method research in DMU environment heterogeneity situation.
机译:古典的DEA假设DMU是同质的,但实际上,一些研究人员发现DMU始终具有异质性质,这将导致相对效率评分的偏差和影响DEA的识别力。环境异质性是DMU异质性的一个特征表现。回顾DMU环境对DEA方法的异质性,并专注于前沿分离方法的缺陷,讨论了多种环境特征变量的DEA方法(简短)分组,并展示了中国公路货运业(简称CRFTI)的方法,并将结果与​​经典DEA方法进行比较,而不是考虑环境异质性以确认辨别力。作者希望在DMU环境异质性情况下提供一些启示录到DEA方法研究。

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