A Comparison of Ensemble and Dimensionality Reduction DEA Models Based on Entropy Criterion

Autor:	Parag C. Pendharkar
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	data envelopment analysis dimensionality reduction ensembles exhaustive state space search entropy Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Algorithms, Vol 13, Iss 9, p 232 (2020)
Druh dokumentu:	article
ISSN:	13090232 1999-4893
DOI:	10.3390/a13090232
Popis:	Dimensionality reduction research in data envelopment analysis (DEA) has focused on subjective approaches to reduce dimensionality. Such approaches are less useful or attractive in practice because a subjective selection of variables introduces bias. A competing unbiased approach would be to use ensemble DEA scores. This paper illustrates that in addition to unbiased evaluations, the ensemble DEA scores result in unique rankings that have high entropy. Under restrictive assumptions, it is also shown that the ensemble DEA scores are normally distributed. Ensemble models do not require any new modifications to existing DEA objective functions or constraints, and when ensemble scores are normally distributed, returns-to-scale hypothesis testing can be carried out using traditional parametric statistical techniques.
Databáze:	Directory of Open Access Journals
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