دو آزمون اماری برای شناسایی نقاط پرت در اندازه گیری عملکرد ناپارامتری

In data envelopment analysis the use of peer set to assess individual or best practice performance, detecting outliers is critical for achieving accurate results. In these deterministic frontier models, statistical tests are now mostly available. This paper deals with two statistical tests for detecting outliers in data envelopment analysis. In the presented methods, each observation is deleted from the sample once and the resulting DEA model is solved, leading to a distribution of efficiency estimates; before and after elimination. Based on the achieved distribution, two statistical tests are then designed to identify the potential outlier(s). We illustrate the method through a real data set. The method could be used in a first step before using any frontier estimation