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Data Envelopment Analysis with Missing Values: an Approach Using Neural Network


B. Dalvand, F. Hosseinzadeh Lotfi, G. R. Jahanshahloo


Vol. 17  No. 2  pp. 29-33


The data envelopment analysis (DEA) models developed with the assumption that input and output data from all of the decision making units (DMUs) to be evaluated are available. So, the need to apply an appropriate approach so that it handles cases includes DMUs whose some data are missing, has been an important issue. In this paper, we consider the case of missing values in one component of the output vector of a certain unit. We first apply a DEA-base clustering method to know the cluster that this unit belongs to and then predicted the missing value by training the neural network algorithm with this cluster. Finally, we also apply EM algorithm and Monte Carlo simulation to compare obtained results by an illustrative example.