Data envelopment Analysis with Missing Data: a Multiple Linear Regression Analysis Approach

Data envelopment analysis (DEA) assumes that the data set is precise when performing e±ciency evaluation of peer decision making units (DMUs). The current paper proposes a multiple linear regression analysis (MLRA) approach to estimate missing values if some of the entries in the data set are missing. Its algorithm to derive the estimations is also proposed. In order to verify the credibility of the proposed approach, an example of 30 US commercial banks is applied to case analysis. Using the proposed algorithm, the e±ciencies of all DMUs are obtained. A Friedman test and a Kendall's Tau rank correlation analysis statistically examine the results. Moreover, the e±ciency interval and e±ciency distribution for a DMU are obtained considering random errors of the estimations. After that, an example of public secondary schools serves to illustrate the applications in the end.