Please use this identifier to cite or link to this item:
http://cmuir.cmu.ac.th/jspui/handle/6653943832/57048
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Varith Pipitpojanakarn | en_US |
dc.contributor.author | Woraphon Yamaka | en_US |
dc.contributor.author | Songsak Sriboonchitta | en_US |
dc.contributor.author | Paravee Maneejuk | en_US |
dc.date.accessioned | 2018-09-05T03:34:20Z | - |
dc.date.available | 2018-09-05T03:34:20Z | - |
dc.date.issued | 2017-11-01 | en_US |
dc.identifier.issn | 19367317 | en_US |
dc.identifier.issn | 19366612 | en_US |
dc.identifier.other | 2-s2.0-85040913141 | en_US |
dc.identifier.other | 10.1166/asl.2017.10142 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040913141&origin=inward | en_US |
dc.identifier.uri | http://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.subject | Computer Science | en_US |
dc.subject | Energy | en_US |
dc.subject | Engineering | en_US |
dc.subject | Environmental Science | en_US |
dc.subject | Mathematics | en_US |
dc.subject | Social Sciences | en_US |
dc.title | Frontier quantile model using a generalized class of skewed distributions | en_US |
dc.type | Journal | en_US |
article.title.sourcetitle | Advanced Science Letters | en_US |
article.volume | 23 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
Files in This Item:
There are no files associated with this item.
Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.