A study on a composite support vector quantile regression with varying coefficient model

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.