An empirical likelihood estimator of stochastic frontier model

© Published under licence by IOP Publishing Ltd. We consider a stochastic frontier model, with independent observation errors identically distributed with an unknown probability density function. Instead of maximizing the parametric version of the likelihood function, which requires knowledge about...

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Main Authors:	Pathairat Pastpipatkul, Woraphon Yamaka, Paravee Maneejuk, Songsak Sriboonchitta
Format:	Conference Proceeding
Published:	2018
Subjects:	Physics and Astronomy
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051386757&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/59114
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-59114
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spelling	th-cmuir.6653943832-591142018-09-05T04:38:41Z An empirical likelihood estimator of stochastic frontier model Pathairat Pastpipatkul Woraphon Yamaka Paravee Maneejuk Songsak Sriboonchitta Physics and Astronomy © Published under licence by IOP Publishing Ltd. We consider a stochastic frontier model, with independent observation errors identically distributed with an unknown probability density function. Instead of maximizing the parametric version of the likelihood function, which requires knowledge about the error distribution, we replace the parametric likelihood with an empirical likelihood. A simulation and experiment study are presented to illustrate the finite-sample of this estimator in terms of its accuracy and robustness. Our proposed estimation is competitive and allows for better analysis of datasets than existing parametric methods. 2018-09-05T04:38:41Z 2018-09-05T04:38:41Z 2018-07-26 Conference Proceeding 17426596 17426588 2-s2.0-85051386757 10.1088/1742-6596/1053/1/012137 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051386757&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/59114
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Physics and Astronomy
spellingShingle	Physics and Astronomy Pathairat Pastpipatkul Woraphon Yamaka Paravee Maneejuk Songsak Sriboonchitta An empirical likelihood estimator of stochastic frontier model
description	© Published under licence by IOP Publishing Ltd. We consider a stochastic frontier model, with independent observation errors identically distributed with an unknown probability density function. Instead of maximizing the parametric version of the likelihood function, which requires knowledge about the error distribution, we replace the parametric likelihood with an empirical likelihood. A simulation and experiment study are presented to illustrate the finite-sample of this estimator in terms of its accuracy and robustness. Our proposed estimation is competitive and allows for better analysis of datasets than existing parametric methods.
format	Conference Proceeding
author	Pathairat Pastpipatkul Woraphon Yamaka Paravee Maneejuk Songsak Sriboonchitta
author_facet	Pathairat Pastpipatkul Woraphon Yamaka Paravee Maneejuk Songsak Sriboonchitta
author_sort	Pathairat Pastpipatkul
title	An empirical likelihood estimator of stochastic frontier model
title_short	An empirical likelihood estimator of stochastic frontier model
title_full	An empirical likelihood estimator of stochastic frontier model
title_fullStr	An empirical likelihood estimator of stochastic frontier model
title_full_unstemmed	An empirical likelihood estimator of stochastic frontier model
title_sort	empirical likelihood estimator of stochastic frontier model
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051386757&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/59114
_version_	1681425191427637248

An empirical likelihood estimator of stochastic frontier model

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