Selecting DEA specifications and ranking units via PCA
Selecting DEA specifications and ranking units via PCA
复制标题
DOI：
10.1057/palgrave.jors.2601705
复制
发表时间：
2004-05-01
影响因子：
3.6
通讯作者：
Molinero, CM
中科院分区：
文献类型：
--
作者：
Cinca, CS;Molinero, CM
数据包络分析（DEA）模型选择存在问题。任何DMU的估计效率取决于模型中包含的输入和输出。它还取决于输出加上输入的数量。显然，选择简洁的规格很重要，并尽可能避免为以不寻常方式运行的dmu分配完全高效评级的模型（特立独行）。提出了一种新的模型选择方法。计算所有可能的DEA模型规格的效率。用主成分分析法对结果进行了分析。结果表明，使用该方法可以很容易地评估模型的等效性或不相似性。特定的dmu在给定的模型规范下实现一定效率水平的原因变得清晰起来。该方法还具有生成DMU排名的额外优势。这些排名总是可以独立于模型是在恒定或可变的规模回报下进行估计而建立的。
Data envelopment analysis (DEA) model selection is problematic. The estimated efficiency for any DMU depends on the inputs and Outputs included in the model. It also depends on the number of outputs plus inputs. It is clearly important to select parsimonious specifications and to avoid as far as possible models that assign full high-efficiency ratings to DMUs that operate in unusual ways (mavericks). A new method for model selection is proposed in this paper. Efficiencies are calculated for all possible DEA model specifications. The results are analysed using Principal Component Analysis. It is shown that model equivalence or dissimilarity can be easily assessed using this approach. The reasons why particular DMUs achieve a certain level of efficiency with a given model specification become clear. The methodololgy has the additional advantage of producing DMU rankings. These rankings can always be established independently of whether the model is estimated under constant or under variable returns to scale.