Frontier quantile model using a generalized class of skewed distributions

© 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 t...

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Main Authors:	Varith Pipitpojanakarn, Woraphon Yamaka, Songsak Sriboonchitta, Paravee Maneejuk
Format:	Journal
Published:	2018
Subjects:	Computer Science Energy Engineering Environmental Science Mathematics Social Sciences
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040913141&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57048
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-57048
record_format	dspace
spelling	th-cmuir.6653943832-570482018-09-05T03:54:24Z Frontier quantile model using a generalized class of skewed distributions Varith Pipitpojanakarn Woraphon Yamaka Songsak Sriboonchitta Paravee Maneejuk Computer Science Energy Engineering Environmental Science Mathematics Social Sciences © 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. 2018-09-05T03:34:20Z 2018-09-05T03:34:20Z 2017-11-01 Journal 19367317 19366612 2-s2.0-85040913141 10.1166/asl.2017.10142 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040913141&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57048
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Computer Science Energy Engineering Environmental Science Mathematics Social Sciences
spellingShingle	Computer Science Energy Engineering Environmental Science Mathematics Social Sciences Varith Pipitpojanakarn Woraphon Yamaka Songsak Sriboonchitta Paravee Maneejuk Frontier quantile model using a generalized class of skewed distributions
description	© 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.
format	Journal
author	Varith Pipitpojanakarn Woraphon Yamaka Songsak Sriboonchitta Paravee Maneejuk
author_facet	Varith Pipitpojanakarn Woraphon Yamaka Songsak Sriboonchitta Paravee Maneejuk
author_sort	Varith Pipitpojanakarn
title	Frontier quantile model using a generalized class of skewed distributions
title_short	Frontier quantile model using a generalized class of skewed distributions
title_full	Frontier quantile model using a generalized class of skewed distributions
title_fullStr	Frontier quantile model using a generalized class of skewed distributions
title_full_unstemmed	Frontier quantile model using a generalized class of skewed distributions
title_sort	frontier quantile model using a generalized class of skewed distributions
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040913141&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57048
_version_	1681424806128386048

Frontier quantile model using a generalized class of skewed distributions

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