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Variable selection in data envelopment analysis via Akaike’s information criteria
- Source :
- Annals of Operations Research. 253:453-476
- Publication Year :
- 2016
- Publisher :
- Springer Science and Business Media LLC, 2016.
-
Abstract
- The decision makers always suffer from predicament in choosing appropriate variable set to evaluate/improve production efficiencies in many applications of data envelopment analysis (DEA). The selected data set may exist information redundancy. On that account, this study proposes an alternative approach to screen out proper input and output variables set for evaluation via Akaike’s information criteria (AIC) rule. This method mainly focuses on assessing the importance of subset of original variables rather than testing the marginal role of variables one by one in many other methods. In terms of the proposed approach, the most optimized variable set contains the least redundant information, which provides decision support to the decision makers. Besides, we also define redundant/cross redundant variables with the form of theorems and give the proofs subsequently. In addition, the AIC approach is firstly extended to stochastic data set to select an appropriate set of stochastic variables as well. Finally, the proposed approach has been applied to some data sets from given data and prior DEA literatures.
- Subjects :
- Decision support system
021103 operations research
05 social sciences
0211 other engineering and technologies
General Decision Sciences
Information Criteria
Feature selection
02 engineering and technology
Management Science and Operations Research
computer.software_genre
Data set
Set (abstract data type)
Variable (computer science)
0502 economics and business
Data envelopment analysis
Data mining
050207 economics
Akaike information criterion
computer
Mathematics
Subjects
Details
- ISSN :
- 15729338 and 02545330
- Volume :
- 253
- Database :
- OpenAIRE
- Journal :
- Annals of Operations Research
- Accession number :
- edsair.doi...........22a3dd410090923ed6b2875a6c3803f8