Publication:
Energy Performance Evaluation of OECD Countries Using Bayesian Stochastic Frontier Analysis and Bayesian Network Classifiers

dc.authorscopusid12766595200
dc.authorscopusid57191925575
dc.authorscopusid36126813200
dc.contributor.authorCengiz, M.A.
dc.contributor.authorDunder, E.
dc.contributor.authorŞenel, T.
dc.date.accessioned2020-06-21T13:17:43Z
dc.date.available2020-06-21T13:17:43Z
dc.date.issued2018
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Cengiz] Mehmet Ali, Department of Statistics, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Dunder] Emre, Department of Statistics, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Şenel] Talat, Department of Statistics, Ondokuz Mayis Üniversitesi, Samsun, Turkeyen_US
dc.description.abstractMore recently a large amount of interest has been devoted to the use of Bayesian methods for deriving parameter estimates of the stochastic frontier analysis. Bayesian stochastic frontier analysis (BSFA) seems to be a useful method to assess the efficiency in energy sector. However, BSFA results do not expose the multiple relationships between input and output variables and energy efficiency. This study proposes a framework to make inferences about BSFA efficiencies, recognizing the underlying relationships between variables and efficiency, using Bayesian network (BN) approach. BN classifiers are proposed as a method to analyze the results obtained from BSFA. © 2017 Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.identifier.doi10.1080/02664763.2016.1257586
dc.identifier.endpage25en_US
dc.identifier.issn0266-4763
dc.identifier.issn1360-0532
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85008624404
dc.identifier.scopusqualityQ2
dc.identifier.startpage17en_US
dc.identifier.urihttps://doi.org/10.1080/02664763.2016.1257586
dc.identifier.volume45en_US
dc.identifier.wosWOS:000415929600003
dc.identifier.wosqualityQ3
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltd. michael.wagreich@univie.ac.aten_US
dc.relation.ispartofJournal of Applied Statisticsen_US
dc.relation.journalJournal of Applied Statisticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBayesianen_US
dc.subjectBayesian Networken_US
dc.subjectEnergyen_US
dc.subjectStochastic Frontier Analysisen_US
dc.titleEnergy Performance Evaluation of OECD Countries Using Bayesian Stochastic Frontier Analysis and Bayesian Network Classifiersen_US
dc.typeArticleen_US
dspace.entity.typePublication

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