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dc.contributor.authorVarith Pipitpojanakarnen_US
dc.contributor.authorWoraphon Yamakaen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorParavee Maneejuken_US
dc.date.accessioned2018-09-05T03:34:20Z-
dc.date.available2018-09-05T03:34:20Z-
dc.date.issued2017-11-01en_US
dc.identifier.issn19367317en_US
dc.identifier.issn19366612en_US
dc.identifier.other2-s2.0-85040913141en_US
dc.identifier.other10.1166/asl.2017.10142en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040913141&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/57048-
dc.description.abstract© 2017 American Scientific Publishers All rights reserved. One of the classical ways to predict manufacturing production is to use Stochastic frontier model. At present, the most accurate predictions obtained by using this model involve the use of quantiles and asymmetric Laplace distributions for the noise and inefficiency. In this paper, we analyze the possibility of using more general skew distributions. We show that skew normal distributions lead to better predictions.en_US
dc.subjectComputer Scienceen_US
dc.subjectEnergyen_US
dc.subjectEngineeringen_US
dc.subjectEnvironmental Scienceen_US
dc.subjectMathematicsen_US
dc.subjectSocial Sciencesen_US
dc.titleFrontier quantile model using a generalized class of skewed distributionsen_US
dc.typeJournalen_US
article.title.sourcetitleAdvanced Science Lettersen_US
article.volume23en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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