Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/59120
Title: Generalized predictive recursion maximum likelihood for robust mixture regression
Authors: Pradon Sureephong
Woraphon Yamaka
Paravee Maneejuk
Keywords: Physics and Astronomy
Issue Date: 26-Jul-2018
Abstract: © 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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051398682&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/59120
ISSN: 17426596
17426588
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.