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A study on a composite support vector quantile regression with varying coefficient model
Journal of the Korean Data & Information Science Society 2018;29:1077-86
Published online July 31, 2018
© 2018 Korean Data and Information Science Society.

Insuk Sohn1 · Jooyong Shim2 · Kyungha Seok3

1Statistics and Data Center, Samsung Medical Center
23Department of Statistics, Inje University
Correspondence to: Professor, Institute of Statistical Information, Department of Statistics, Inje University, Gyungnam 50834, Korea. statskh@inje.ac.kr
Received June 1, 2018; Revised June 25, 2018; Accepted June 29, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Varying coefficient models are widely used to explore dynamic patterns of regression parameters among regression models available to avoid the curse of dimensionality. In this paper we propose a new regression estimation of the varying coefficient composite support vector quantile regression which combines the formulations of the composite quantile regression and the varyng coefficient support vector quantile regression which is a nonparametric quantile regression with varying regression quantiles. We also consider a cross validation method for the optimal values of hyperparameters which affect the performance of the proposed method. Numerical studies with synthetic and real data are conducted to illustrate the performance of the proposed estimation of the regression functions.
Keywords : Composite quantile regression, cross validation function, quantile regression, support vector quantile regression, varying coefficient model.