Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/71557
Title: Entropy inference in smooth transition kink regression
Authors: Paravee Maneejuk
Woraphon Yamaka
Songsak Sriboonchitta
Keywords: Mathematics
Issue Date: 1-Jan-2020
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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85093970479&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/71557
ISSN: 15324141
03610918
Appears in Collections:CMUL: Journal Articles

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