Data envelopment analysis with missing data
2021
Missing data is a chronic disease in applications of data envelopment analysis. Very often, important input or output variables are not completely specified and/or the decision-making units do not report all the required statistics. Therefore, the missing values in the inputs and outputs cannot be studied using the original data envelopment analysis models. This paper introduces methods for finding missing data when the existing data is certain. In this article, after explaining the essential concepts of missing values, we describe some methods of missing value imputation that reduce the complexity of data analysis. There are several methods for imputing missing data, including various methods of simple imputation and multiple imputation. This paper is the first systematic attempt to utilize data containing missing values using statistical approaches in the DEA. In particular, we examine what happens if we keep empty entries in the data set and assign a certain numeric value to them. To show how the proposed methods work, they will be used to evaluate a set of secondary public schools in Greece in some of which there are missing input or output values.
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