Semi-functional partial linear quantile regression

Abstract

Semi-functional partial linear model is a flexible model in which a scalar response is related to both functional covariate and scalar covariates. We propose a quantile estimation of this model as an alternative to the least square approach. We also extend the proposed method to kNN quantile method. Under some regular conditions, we establish the asymptotic normality of quantile estimators of regression coefficient. We also derive the rates of convergence of nonparametric function. Finite-sample performance of our estimation is compared with least square approach via a Monte Carlo simulation study. The simulation results indicate that our method is much more robust than the least square method. A real data example about spectrometric data is used to illustrate that our model and approach are promising.