2015/03/09 by Dariush Khezrimotlagh, Khezrimotlagh, Dariush · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Advanced Statistical Methods and Models #Economic and Technological Innovation #Efficiency Analysis Using DEA #FOS: Mathematics #Fiscal Policy and Economic Growth #Optimization and Control (math.OC) #math.OC
paper · pdf · doi:10.48550/arxiv.1503.02548
12 pages, 7 figures
arxiv created 2015/03/09 · openalex publication_date 2015/03/09 · arxiv updated 2015/03/10 · openalex created_date 2022/08/31 · openalex updated_date 2026/07/28
One of the concerns about using non-parametric estimators such as Data Envelopment Analysis (DEA), is the presence of outliers. There are a good number of studies that mention this assessment in the literature of DEA, however, there is no clear definition to identify what outliers are in DEA. Moreover, most of the studies have used additional procedures which have high computational complexities. This paper proposes a suitable definition to identify outliers as well as a simple methodology to illustrate how DEA, by using Kourosh and Arash Method (KAM), is easily able to detect outliers without using additional technologies and their computational complexities. The methodology of detecting outliers by KAM is represented with an example which was used in previous research to depict DEA's weakness of detecting outliers. The results clearly reject this claim that DEA is not able to detect outliers.