NDLI: Variable selection in data envelopment analysis via Akaike’s information criteria

Content Provider	Springer Nature Link
Author	Li, Yongjun Shi, Xiao Yang, Min Liang, Liang
Copyright Year	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.
Starting Page	453
Ending Page	476
Page Count	24
File Format	PDF
ISSN	02545330
Journal	Annals of Operations Research
Volume Number	253
Issue Number	1
e-ISSN	15729338
Language	English
Publisher	Springer US
Publisher Date	2016-11-30
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data envelopment analysis (DEA) Variables set selection Akaike’s information criteria (AIC) Operation Research/Decision Theory Combinatorics Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Management Science and Operations Research

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