Generalized predictive recursion maximum likelihood for robust mixture regression

Generalized predictive recursion maximum likelihood for robust mixture regression

© Published under licence by IOP Publishing Ltd. In the application of econometric model, the error distribution is unknown and is not easily to specify in the likelihood function. In some situations, there might exist a mixture distribution in the errors and thus the traditional estimation method w...

Full description

Saved in:
Bibliographic Details
Main Authors: Pradon Sureephong, Woraphon Yamaka, Paravee Maneejuk
Format: Conference Proceeding
Published: 2018
Subjects:
Physics and Astronomy
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051398682&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/59120
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Chiang Mai University
id th-cmuir.6653943832-59120
record_format dspace
spelling th-cmuir.6653943832-591202018-09-05T04:38:46Z Generalized predictive recursion maximum likelihood for robust mixture regression Pradon Sureephong Woraphon Yamaka Paravee Maneejuk Physics and Astronomy © Published under licence by IOP Publishing Ltd. In the application of econometric model, the error distribution is unknown and is not easily to specify in the likelihood function. In some situations, there might exist a mixture distribution in the errors and thus the traditional estimation method would probably yield a biased result. In this study, this mixture distribution of the error term is taken into account and the generalized semiparametric estimation is presented and applied in regression model. We also use an experiment study and the real application analysis to check the performance of this estimator in regression model. The performance of this estimation is then compared with that of conventional Least Squares method in the real data analysis. 2018-09-05T04:38:46Z 2018-09-05T04:38:46Z 2018-07-26 Conference Proceeding 17426596 17426588 2-s2.0-85051398682 10.1088/1742-6596/1053/1/012133 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051398682&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/59120
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Physics and Astronomy
spellingShingle Physics and Astronomy
Pradon Sureephong
Woraphon Yamaka
Paravee Maneejuk
Generalized predictive recursion maximum likelihood for robust mixture regression
description © Published under licence by IOP Publishing Ltd. In the application of econometric model, the error distribution is unknown and is not easily to specify in the likelihood function. In some situations, there might exist a mixture distribution in the errors and thus the traditional estimation method would probably yield a biased result. In this study, this mixture distribution of the error term is taken into account and the generalized semiparametric estimation is presented and applied in regression model. We also use an experiment study and the real application analysis to check the performance of this estimator in regression model. The performance of this estimation is then compared with that of conventional Least Squares method in the real data analysis.
format Conference Proceeding
author Pradon Sureephong
Woraphon Yamaka
Paravee Maneejuk
author_facet Pradon Sureephong
Woraphon Yamaka
Paravee Maneejuk
author_sort Pradon Sureephong
title Generalized predictive recursion maximum likelihood for robust mixture regression
title_short Generalized predictive recursion maximum likelihood for robust mixture regression
title_full Generalized predictive recursion maximum likelihood for robust mixture regression
title_fullStr Generalized predictive recursion maximum likelihood for robust mixture regression
title_full_unstemmed Generalized predictive recursion maximum likelihood for robust mixture regression
title_sort generalized predictive recursion maximum likelihood for robust mixture regression
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051398682&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/59120
_version_ 1681425192532836352

Similar Items