Estimation in Functional Partial Linear Composite Quantile Regression Model

This paper studies estimation in functional partial linear composite quantile regression model in which the dependent variable is related to both a function-valued random variable in linear form and a real-valued random variable in nonparametric form. The functional principal component analysis and regression splines are employed to estimate the slope function and the nonparametric function respectively, and the convergence rates of the estimators are obtained under some regularity conditions. Simulation studies and a real data example are presented for illustration of the performance of the proposed estimators.