English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of the concerns about using non-parametric estimators such as DataEnvelopment Analysis (DEA), is the presence of outliers. There are a goodnumber 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 highcomputational complexities. This paper proposes a suitable definition toidentify outliers as well as a simple methodology to illustrate how DEA, byusing Kourosh and Arash Method (KAM), is easily able to detect outliers withoutusing additional technologies and their computational complexities. Themethodology of detecting outliers by KAM is represented with an example whichwas 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.

How to detect outliers in data envelopment analysis by Kourosh and Arash method