Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58519
Title: Asymmetric effect with quantile regression for interval-valued variables
Authors: Teerawut Teetranont
Woraphon Yamaka
Songsak Sriboonchitta
Keywords: Computer Science
Issue Date: 1-Jan-2018
Abstract: © Springer International Publishing AG 2018. In this paper, we propose a quantile regression with interval valued data using a convex combination method. The model we propose generalizes series of existing models, say typically with the center method. Three estimation techniques consisting EM algorithm, Least squares, Lasso penalty are presented to estimate the unknown parameters of our model. A series of Monte Carlo experiments are conducted to assess the performance of our proposed model. The results support our theoretical properties. Finally, we apply our model to empirical data in order to show the usefulness of the proposed model. The results imply that the EM algorithm provides a best fit estimation for our data set and captures the effect of oil differently across various quantile levels.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037872340&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58519
ISSN: 1860949X
Appears in Collections:CMUL: Journal Articles

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