In data envelopment analysis (DEA), the input and output data must be complete for the comparison of decision-making units (DMU). Some data may be missing due to various reasons. In such cases, examination of the DMUs with missing data is excluded. Excluding of the examination of some decision-making units may change the effectiveness of other decision-making units. To estimate missing observations as closely to the actual value as possible may be more useful than excluding of the examination of decision-making units. In this study, a problem with the complete input and output data was selected from the DEA literature, and then some data were randomly deleted and turned into a problem with missing data. After the missing data had been estimated with the Expectation Maximization (EM) Algorithm, problems with missing and complete data were examined using DEA and the results were compared. Keywords: Missing Value, Data Envelopment Analysis, Expectation Maximization Algorithm

Username
Password