A statistical test for detecting influential observations in DEA

A statistical alternative approach to the problem of detecting influential observations in nonparametric deterministic Data Envelopment Analysis models is proposed. It is designed to classify those sample observations which appear as influential. The analyst will then have to check whether these observations are contaminated by data errors or not. Further, this method determines when efficiency changes owing to the presence of a given unit in the sample are statistically important.