Home > Research > Publications & Outputs > Clustering and Meta-Envelopment in Data Envelop...

Electronic data

  • Draft_revised_4_BLIND

    Rights statement: This is the author’s version of a work that was accepted for publication in Physics Reports. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Physics Reports, 373, 4-5, 2022 DOI: 10.1016/S0370-1573(02)00269-7

    Accepted author manuscript, 1.3 MB, PDF document

    Embargo ends: 20/04/24

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Clustering and Meta-Envelopment in Data Envelopment Analysis

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>20/04/2022
<mark>Journal</mark>European Journal of Operational Research
Number of pages16
Publication StatusE-pub ahead of print
Early online date20/04/22
<mark>Original language</mark>English

Abstract

We propose techniques of classification of a potentially heterogeneous data set into groups in a way that is consistent with the intended purpose of the clustering, which is Data Envelopment Analysis (DEA). Using standard clustering techniques and then applying DEA is shown to be sub-optimal in many instances of empirical relevance. Our methods are based on a novel interpretation and implementation of convex nonparametric least squares (CNLS) which allows not only classification into different clusters but also finding the number of clusters from the data. Moreover, we provide techniques for model validation in CNLS regarding the allocation into groups using efficiency criteria. We provide a prior designed to minimize variation within groups and maximize variation across groups. The new techniques are examined using Monte Carlo experiments and they are applied to a data set of large U.S. banks. Additionally, we propose new techniques for meta-envelopment or meta-frontier formulations in efficiency analysis.