A generalized information theoretical approach to non-linear time series model

© Springer International Publishing AG 2017. The limited data will bring about an underdetermined, or ill-posed problem for the observed data, or for regressions using small data set with limited data and the traditional estimation techniques are difficult to obtain the optimal solution. Thus the ap...

Full description

Saved in:
Bibliographic Details
Main Authors: Songsak Sriboochitta, Woraphon Yamaka, Paravee Maneejuk, Pathairat Pastpipatkul
Format: Book Series
Published: 2018
Subjects:
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012919655&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/57110
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Chiang Mai University
id th-cmuir.6653943832-57110
record_format dspace
spelling th-cmuir.6653943832-571102018-09-05T03:35:08Z A generalized information theoretical approach to non-linear time series model Songsak Sriboochitta Woraphon Yamaka Paravee Maneejuk Pathairat Pastpipatkul Computer Science © Springer International Publishing AG 2017. The limited data will bring about an underdetermined, or ill-posed problem for the observed data, or for regressions using small data set with limited data and the traditional estimation techniques are difficult to obtain the optimal solution. Thus the approach of Generalized Maximum Entropy (GME) is proposed in this study and applied it to estimate the kink regression model under the limited information situation. To the best of our knowledge, the estimation of kink regression model using GME has been not done yet. Hence, we extend the entropy linear regression to non-linear kink regression by modifying the objective and constraint functions under the context of GME. We use both Monte Carlo simulation and real data study to evaluate the performance of our estimation from Kink regression and found that GME estimator performs slightly better compared to the traditional Least squares and Maximum likelihood estimators. 2018-09-05T03:35:08Z 2018-09-05T03:35:08Z 2017-02-01 Book Series 1860949X 2-s2.0-85012919655 10.1007/978-3-319-50742-2_20 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012919655&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57110
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
spellingShingle Computer Science
Songsak Sriboochitta
Woraphon Yamaka
Paravee Maneejuk
Pathairat Pastpipatkul
A generalized information theoretical approach to non-linear time series model
description © Springer International Publishing AG 2017. The limited data will bring about an underdetermined, or ill-posed problem for the observed data, or for regressions using small data set with limited data and the traditional estimation techniques are difficult to obtain the optimal solution. Thus the approach of Generalized Maximum Entropy (GME) is proposed in this study and applied it to estimate the kink regression model under the limited information situation. To the best of our knowledge, the estimation of kink regression model using GME has been not done yet. Hence, we extend the entropy linear regression to non-linear kink regression by modifying the objective and constraint functions under the context of GME. We use both Monte Carlo simulation and real data study to evaluate the performance of our estimation from Kink regression and found that GME estimator performs slightly better compared to the traditional Least squares and Maximum likelihood estimators.
format Book Series
author Songsak Sriboochitta
Woraphon Yamaka
Paravee Maneejuk
Pathairat Pastpipatkul
author_facet Songsak Sriboochitta
Woraphon Yamaka
Paravee Maneejuk
Pathairat Pastpipatkul
author_sort Songsak Sriboochitta
title A generalized information theoretical approach to non-linear time series model
title_short A generalized information theoretical approach to non-linear time series model
title_full A generalized information theoretical approach to non-linear time series model
title_fullStr A generalized information theoretical approach to non-linear time series model
title_full_unstemmed A generalized information theoretical approach to non-linear time series model
title_sort generalized information theoretical approach to non-linear time series model
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012919655&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/57110
_version_ 1681424817470832640