The bootstrap approach for DEA model with undesirable outputs

In this paper, the DEA models with undesirable outputs are considered and an effective bootstrap approach is developed for statistical inference. The most attractive point for our method is that it not only can allow statistical noise and inherent dependency to be investigated simultaneously, but also can further enhance the statistical foundation of DEA analysis with the case of undesirable outputs. Finally, an empirical example is used to illustrate our methodology proposed above.