Robust Data Envelopment Analysis
نویسنده
چکیده
Data envelopment analysis (DEA) is a nonstochastic and nonparametric linear programming technique where a set of units are evaluated according to their input consumption and output production (Charnes et al. (1978)). Given that DEA efficiency analysis can be influenced by the presence of outliers, Banker and Chang (2006) proposed a method to detect atypical units through the super-efficiency model (Banker and Gifford (1988)). When relying on super-efficiency, masking and swamping can affect the analysis. In order to overwhelm these drawbacks, we propose a DEA based on the forward search framework originally introduced by Atkinson and Riani (2000). We carry out the analysis on a simulated Cobb-Douglas production function and we contaminate the dataset checking the robustness of our statistical model. Stressing the importance to find theoretical boundaries for the inference on outliers, we build up envelopes through Monte Carlo simulations.
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