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...

Full description

Saved in:

Bibliographic Details
Main Authors:	Varith Pipitpojanakarn, Woraphon Yamaka, Songsak Sriboonchitta, Paravee Maneejuk
Format:	Journal
Published:	2018
Subjects:	Energy Engineering Environmental Science Mathematics Agricultural and Biological Sciences Arts and Humanities
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040913141&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/46638
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Chiang Mai University

id	th-cmuir.6653943832-46638
record_format	dspace
spelling	th-cmuir.6653943832-466382018-04-25T07:36:10Z Frontier quantile model using a generalized class of skewed distributions Varith Pipitpojanakarn Woraphon Yamaka Songsak Sriboonchitta Paravee Maneejuk Energy Engineering Environmental Science Mathematics Agricultural and Biological Sciences Arts and Humanities © 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. 2018-04-25T06:58:43Z 2018-04-25T06:58:43Z 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/6653943832/46638
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Energy Engineering Environmental Science Mathematics Agricultural and Biological Sciences Arts and Humanities
spellingShingle	Energy Engineering Environmental Science Mathematics Agricultural and Biological Sciences Arts and Humanities 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/6653943832/46638
_version_	1681422911565463552

Frontier quantile model using a generalized class of skewed distributions

Similar Items