Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/70439
Title: Bayesian Estimation of Archimedean Copula-Based sur Quantile Models
Authors: Nachatchapong Kaewsompong
Paravee Maneejuk
Woraphon Yamaka
Keywords: Computer Science
Issue Date: 1-Jan-2020
Abstract: © 2020 Nachatchapong Kaewsompong et al. We propose a high-dimensional copula to model the dependence structure of the seemingly unrelated quantile regression. As the conventional model faces with the strong assumption of the multivariate normal distribution and the linear dependence structure, thus, we apply the multivariate exchangeable copula function to relax this assumption. As there are many parameters to be estimated, we consider the Bayesian Markov chain Monte Carlo approach to estimate the parameter interests in the model. Four simulation studies are conducted to assess the performance of our proposed model and Bayesian estimation. Satisfactory results from simulation studies are obtained suggesting the good performance and reliability of the Bayesian method used in our proposed model. The real data analysis is also provided, and the empirical comparison indicates our proposed model outperforms the conventional models in all considered quantile levels.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85089021591&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/70439
ISSN: 10990526
10762787
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.