Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/71557
Full metadata record
DC FieldValueLanguage
dc.contributor.authorParavee Maneejuken_US
dc.contributor.authorWoraphon Yamakaen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2021-01-27T03:54:39Z-
dc.date.available2021-01-27T03:54:39Z-
dc.date.issued2020-01-01en_US
dc.identifier.issn15324141en_US
dc.identifier.issn03610918en_US
dc.identifier.other2-s2.0-85093970479en_US
dc.identifier.other10.1080/03610918.2020.1836214en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85093970479&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/71557-
dc.description.abstract© 2020 Taylor & Francis Group, LLC. This study proposes a smooth transition kink regression model to capture the nonlinear relationship between dependent and independent variables. Our model generalizes that considered in Hansen to allow the continuous regression to be smoothed at any threshold or kink points. We allow the kink effects to be different for all relationships between each independent variable and the dependent variable. Also, in some cases, the regression typed model may have ill-posed problems (if the number of unknown parameters exceeds the number of observations or the underlying distribution is unknown). Therefore, Generalized Maximum Entropy (GME) estimation is applied for estimating our model. This study conducts experiments based on both simulation and real dataset, with comparison to multiple traditional estimations, including the standard Least Squares, Bayesian, and Maximum Likelihood. Experimental results show that the GME estimation is a useful tool for parameter estimates. Simulations also reveal excellent finite sample properties of the suggested method of estimation where the data is limited, and non-normal distribution is held.en_US
dc.subjectMathematicsen_US
dc.titleEntropy inference in smooth transition kink regressionen_US
dc.typeJournalen_US
article.title.sourcetitleCommunications in Statistics: Simulation and Computationen_US
article.stream.affiliationsChiang Mai Universityen_US
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.